Lipids are animportantfoodcomponentformosquitolarvaebecause theyprovide a concentrat‐ ed form of energy storage and a source of essential biochemical nutrients. Fatty acid (FA) constituents of lipids are present in a great structural variety, and are increasingly being used as chemical markers of biogeochemical processes and trophic relationships [147]. While the saturated palmitic acid (16:0) is often one of the most abundant fatty acids in lipid extracts, the interest of nutritional studies has concentrated on polyunsaturated fatty acids (PUFA) with two or more double bonds [148]. Some of these PUFAs are essential to the normal function of cells and they or their corresponding precursors have to be obtained in animal diets. In most ani‐ mals, the 18-carbon chain, 18C, PUFAs can be converted to the longer-chain essential PUFAs, specifically arachidonic acid, ARA, eicosapentaenoic acid, EPA, and docosahexaenoic acid, DHA. Mosquitoes seem to be an exception because their dietary FA requirements cannot be satisfied by the C-18 PUFAs [149, 150]. They require some 20- and 22-C polyunsaturated fatty acids, EPA, ARA and DHA and without an adequate supply of these PUFAs they are not able to fly [149, 150]. Adult females may get these from a blood meal [151] but these PUFAs are be‐ lievedessentialinthelarvalstageforflightmuscledevelopment.Theunderstandingofthespatial andtemporaldistributionofdietaryresources available tomosquitolarvae isneededinorderto clarify the relationship among food availability, vector competence, and mosquito fitness. Not only does the nutrient availability within the habitat have to meet a minimum dietary require‐ ment for proper larval development, but the food consumed in the larval stage is critical for a number of physiological processes that impact adult performance [152].

Kominkova et al. [153], in order to reveal the importance of feeding habitats for the nutrition of anophelinelarvae,analyzedtheFAcompositionoflarvaeofthreemalariatransmittingmosquito

species *An. albimanus, An. vestitipennis* and *An. darlingi* and their corresponding habitats. They found that habitats were generally low in essential PUFAs and there were no significant differences among the FA composition of habitat samples. However, there were significant differences in FA composition of larvae. *Anopheles darlingi* contained significantly higher amounts of FA, specifically the linoleic acid. Large differences in PUFA content were found between field collected and laboratory-reared *An. vestitipennis* larvae, however, there were no differences in the total dry weight of the 4th stage larvae between the wild vs. laboratory-reared populations. Total FA in both larvae and samples of habitats of *An. albimanus* and *An. darlingi* were positively correlated with the concentration of particulate organic carbon and nitrogen (POC, PON) in their respective habitats, but no such correlation was found for *An. vestitipennis*. This study revealed that PUFA are a good indicator of nutritional quality although factors controllingthesuccessofanophelinedevelopmentinlarvalhabitatsarelikelytobemorecomplex and include, among others, the presence of predators, pathogens and toxins.

#### **3.9. Species interactions (predation and competition)**

Understanding species interactions such as competition and predation, across environmental gradients provides insight into how assemblages of mosquitoes are structured. This informa‐ tion is then critical for proper application of biological control [154]. The topic of competition and predation is a good example of the dichotomy in the approach to studying larval stages of mosquitoes. Many papers focus on use of predators for larval control [155-157]. There is a lack of studies focusing on larval competition and predation in the ecological context such as habitat size and temporal stability. But it is what influences the prevalence, pattern, and effects of species interactions across freshwater communities [158-160]. Spatial variation in biotic interactions can explain spatial variation in larval mosquito densities and ultimately the abundance of adult mosquitoes [78, 158]. Studies on predators of mosquito larvae go way back into history. Hinman [161] in his summary of predators on mosquito larvae lists over 100 references. Competition on the other hand is less studied even though interspecific competition for limited resources can be quite important and has been shown to have large effects on mosquito larvae. Mosquitoes compete with tadpoles [162, 163], other species of mosquitoes [164] and cladocerans [165].

Relative impacts of competition and predation change across a gradient of habitat size and permanence [159]. Bodies of water that may serve as larval habitats form a gradient from small and highly ephemeral to large and permanent. At the small, ephemeral end of this gradient, large long-lived predatory organisms (namely fish) are often absent, and aquatic organisms need to develop quickly. These conditions favor rapid growth and development, active foraging, movement, and competitive ability. As water bodies become larger and temporally more stable they can support more diverse community of larger, longer-lived predators. This increase of diversity, number, and voracity of predators favors refuge use, inconspicuousness, predator deterrence, and slow growth and development [159]. Organization of mosquito communities can be viewed in the same way. Interspecific competition among mosquitoes can be more important as a determinant of community structure in small ephemeral habitats, whereas predation can be more important in large permanent habitats [159]. Limited evidence suggests interspecific competition and cannibalism among mosquitoes is common in small pools [70], but comprehensive review of the ecology of competitive interactions of mosquitoes is lacking.

species *An. albimanus, An. vestitipennis* and *An. darlingi* and their corresponding habitats. They found that habitats were generally low in essential PUFAs and there were no significant differences among the FA composition of habitat samples. However, there were significant differences in FA composition of larvae. *Anopheles darlingi* contained significantly higher amounts of FA, specifically the linoleic acid. Large differences in PUFA content were found between field collected and laboratory-reared *An. vestitipennis* larvae, however, there were no differences in the total dry weight of the 4th stage larvae between the wild vs. laboratory-reared populations. Total FA in both larvae and samples of habitats of *An. albimanus* and *An. darlingi* were positively correlated with the concentration of particulate organic carbon and nitrogen (POC, PON) in their respective habitats, but no such correlation was found for *An. vestitipennis*. This study revealed that PUFA are a good indicator of nutritional quality although factors controllingthesuccessofanophelinedevelopmentinlarvalhabitatsarelikelytobemorecomplex

Understanding species interactions such as competition and predation, across environmental gradients provides insight into how assemblages of mosquitoes are structured. This informa‐ tion is then critical for proper application of biological control [154]. The topic of competition and predation is a good example of the dichotomy in the approach to studying larval stages of mosquitoes. Many papers focus on use of predators for larval control [155-157]. There is a lack of studies focusing on larval competition and predation in the ecological context such as habitat size and temporal stability. But it is what influences the prevalence, pattern, and effects of species interactions across freshwater communities [158-160]. Spatial variation in biotic interactions can explain spatial variation in larval mosquito densities and ultimately the abundance of adult mosquitoes [78, 158]. Studies on predators of mosquito larvae go way back into history. Hinman [161] in his summary of predators on mosquito larvae lists over 100 references. Competition on the other hand is less studied even though interspecific competition for limited resources can be quite important and has been shown to have large effects on mosquito larvae. Mosquitoes compete with tadpoles [162, 163], other species of mosquitoes

Relative impacts of competition and predation change across a gradient of habitat size and permanence [159]. Bodies of water that may serve as larval habitats form a gradient from small and highly ephemeral to large and permanent. At the small, ephemeral end of this gradient, large long-lived predatory organisms (namely fish) are often absent, and aquatic organisms need to develop quickly. These conditions favor rapid growth and development, active foraging, movement, and competitive ability. As water bodies become larger and temporally more stable they can support more diverse community of larger, longer-lived predators. This increase of diversity, number, and voracity of predators favors refuge use, inconspicuousness, predator deterrence, and slow growth and development [159]. Organization of mosquito communities can be viewed in the same way. Interspecific competition among mosquitoes can be more important as a determinant of community structure in small ephemeral habitats, whereas predation can be more important in large permanent habitats [159]. Limited evidence

and include, among others, the presence of predators, pathogens and toxins.

**3.9. Species interactions (predation and competition)**

410 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

[164] and cladocerans [165].

Natural predators of mosquito larvae are quite diverse and include the tadpole stages of amphibians [166], planktivorous fishes [165] and aquatic insects (Coleoptera, adult Hetero‐ ptera and larval Odonata). There is a range of papers reviewing predators on mosquito larvae and their potential use in biological control. Kumar and Hwang [167] provided an excellent review of larvicidal efficiency of amphibian tadpoles, larvivorous fish, cyclopoid copepods and aquatic insects. Mogi [168] reviewed insects and invertebrate predation on different life stages of mosquito. Quiroz-Martinez and Rodriguez-Castro [169] summarized the information on arthropods (insects, mites and spiders) that prey on mosquito larvae and discussed the potential of these predators in mosquitoes' biological control programs. Shaalan and Canyon's [156] review covered the predation of different insect species on mosquito larvae, predator prey-habitat relationships, co-habitation developmental issues, survival and abundance, oviposition avoidance, predatorial capacity and integrated vector control. Rozendaal [170] and Chandra et al [171] reviewed information on different larvivorous fish species and the present status of their use in mosquito control.

Despite thorough reviews and much information on different types of predators, there is a paucity of well-designed experimental studies verifying the long term effect of predators on mosquito populations. Although predation has been suggested as one of the important regulation mechanisms for malaria vectors in long lasting aquatic habitats, the predatory efficiency of potential predators is largely unknown [22, 157]. Research on predation of mosquito larvae has relied partly on the identification of larvae in the predators' gut – serological methods [172, 173], partly on correlative field observations evaluating the abun‐ dance of larvae and predators in the habitats [52, 174], and partly on laboratory feeding studies [157]. However, many predators that have been shown to be highly successful in eliminating target prey in the laboratory do not show a similar response in their natural habitats [75, 155]. The most basic question is whether predators have an important impact on mosquito popu‐ lations in the field in the presence of alternative prey. Collins and Resh [118] listed the ecological factors affecting predation that should be considered when designing predation experiments: 1) dietary preference for mosquitoes, 2) abundance of alternative pray; 3) degree of congruity between habitats of the predator and target mosquito; 4) density of predators within habitat; 5) density of mosquito population; 6) quality of habitat as a refuge from predator. Among examples of well-designed experimental studies on multiple predator impacts we can cite Kumar et al [155] who compared the control potential of three larvivorous predators commonly co-occurring in the wetlands of tropical and subtropical regions, the mosquito fish *Gambusia affinis,* the cyclopoid copepod *Mesocyclops aspericornis*, and naiads of the dragonfly *Zyxomma petiolatum*, against the larvae of *An. stephensi* in the presence of alternative cladoceran prey. The presence of the alternative prey significantly reduced larval consumption by all three predators. Kumar et al [155] also discuss the issues related to using non-native mosquito fish considering its potential negative impacts on native assemblages and its lower selectivity for mosquito larvae.

Mosquito control using fish has focused on a limited number of species, primarily *Gambusia af*fi*nis* and *Poecilia reticulata* that have traditionally been used for controlling mosquito larvae [175, 176]. One of the most important concerns when introducing exotic fish for mosquito control is their impact on native species [177] and thus information on the predation role of native species is desirable. Louca et al [175] evaluated the role of larval predation by native fishes in Gambia River and they pointed out that the major impact on larvae was actually exerted by a detritivorous *Tilapia*, which is a prevailing species in the system that feeds on larvae only opportunistically in small aquatic habitats.

Blaustein [134] documented an unefficient control of anopheline larvae in the rice fields in California. He pointed out that contrary to what a good system should be composed of, i.e., a relatively permanent habitat, a specialist control agent and a relatively abundant pest species, the fish-mosquito-rice field system does not have any of these attributes. In addition, mosquito fish may have indirect positive effects on mosquito abundance; they also feed on invertebrates which are either natural predators (see [178]) or potential competitors of mosquito immatures [165]. Thus, this strategy attempts to control a relatively rare prey species with a generalist predator. The underlying mechanisms of predator-prey relationships need to be more clearly defined in order to use this biological control agent more effectively. There is a general need for field experiments on competition, predation, and mutualism, and on their context depend‐ ence across species and habitats [159].

Predation at larval stages can have important evolutionary consequences for mosquitoes [179]. For example, the predation of aquatic immature stages has been identified as a major evolution‐ ary force driving habitat segregation and niche partitioning in the malaria mosquito *An. gambiae* in humid savannahs of West Africa [160, 180]. These studies explored behavioral responses to thepresenceofapredatorinwildpopulationsoftheMandSmolecularformsthattypicallybreed in permanent (e.g., rice field paddies) and temporary (e.g., road ruts) water collections. The experiments showed that the M and S forms modify their behavior in the presence of a natural predator by becoming less active and positioning themselves at the wall of the container. These behavioral modifications suggest that mosquitoes are able to detect a predator's presence, through as yet unknown mechanisms which deserve further investigation.

#### **4. Habitat selection**

Habitat selection, defined as a process in which individuals preferentially choose and occupy a nonrandom set of available habitats, is of major importance for interpretation of spatial and temporal distributions of populations [139, 181]. The choice for suitable places for female mosquitoes to lay eggs is a key-factor for the survival of immature stages (eggs and larvae). Oviposition site selection has been recognized as critical both for the survival and population dynamics of mosquitoes. It is influenced by several environmental factors [182], including the salinity and turbidity of the water, the size and degree of permanence of the water body, the amount of sunlight, the presence of emergent/floating vegetation and shade, presence of predators, and distance to human habitation [8, 66]. In general, larvae of anopheline mosqui‐ toes prefer clean rather than polluted water [8, 183], although in urban areas in parts of Africa *An. gambiae* appears to be adapting to new habitats such as rubbish-filled pools, sometimes containing sewage [182, 184]. Larvae of several Asian species (*An. dirus, An. punctulatus, An. subpictus*) have been reported from muddy and/or polluted waters [63].

Mosquito control using fish has focused on a limited number of species, primarily *Gambusia*

Blaustein [134] documented an unefficient control of anopheline larvae in the rice fields in California. He pointed out that contrary to what a good system should be composed of, i.e., a relatively permanent habitat, a specialist control agent and a relatively abundant pest species, the fish-mosquito-rice field system does not have any of these attributes. In addition, mosquito fish may have indirect positive effects on mosquito abundance; they also feed on invertebrates which are either natural predators (see [178]) or potential competitors of mosquito immatures [165]. Thus, this strategy attempts to control a relatively rare prey species with a generalist predator. The underlying mechanisms of predator-prey relationships need to be more clearly defined in order to use this biological control agent more effectively. There is a general need for field experiments on competition, predation, and mutualism, and on their context depend‐

Predation at larval stages can have important evolutionary consequences for mosquitoes [179]. For example, the predation of aquatic immature stages has been identified as a major evolution‐ ary force driving habitat segregation and niche partitioning in the malaria mosquito *An. gambiae* in humid savannahs of West Africa [160, 180]. These studies explored behavioral responses to thepresenceofapredatorinwildpopulationsoftheMandSmolecularformsthattypicallybreed in permanent (e.g., rice field paddies) and temporary (e.g., road ruts) water collections. The experiments showed that the M and S forms modify their behavior in the presence of a natural predator by becoming less active and positioning themselves at the wall of the container. These behavioral modifications suggest that mosquitoes are able to detect a predator's presence,

Habitat selection, defined as a process in which individuals preferentially choose and occupy a nonrandom set of available habitats, is of major importance for interpretation of spatial and temporal distributions of populations [139, 181]. The choice for suitable places for female mosquitoes to lay eggs is a key-factor for the survival of immature stages (eggs and larvae). Oviposition site selection has been recognized as critical both for the survival and population dynamics of mosquitoes. It is influenced by several environmental factors [182], including the salinity and turbidity of the water, the size and degree of permanence of the water body, the amount of sunlight, the presence of emergent/floating vegetation and shade, presence of predators, and distance to human habitation [8, 66]. In general, larvae of anopheline mosqui‐ toes prefer clean rather than polluted water [8, 183], although in urban areas in parts of Africa *An. gambiae* appears to be adapting to new habitats such as rubbish-filled pools, sometimes

through as yet unknown mechanisms which deserve further investigation.

larvae only opportunistically in small aquatic habitats.

412 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

ence across species and habitats [159].

**4. Habitat selection**

*nis* and *Poecilia reticulata* that have traditionally been used for controlling mosquito larvae [175, 176]. One of the most important concerns when introducing exotic fish for mosquito control is their impact on native species [177] and thus information on the predation role of native species is desirable. Louca et al [175] evaluated the role of larval predation by native fishes in Gambia River and they pointed out that the major impact on larvae was actually exerted by a detritivorous *Tilapia*, which is a prevailing species in the system that feeds on

*af*fi

> In choosing sites for oviposition, females have to consider multiple—and possibly conflicting —factors to arrive at a site selection strategy that will optimize their reproductive success [185]. As many other oviparous species, mosquitoes also avoid oviposition in habitats with high risk of predation to their larvae [154, 186]. Females perceive these different characteristics of their habitats through a set of various cues both positive and negative. Among positive cues, volatile substances released from larval habitats have been implicated as potential olfactory cues mediating oviposition [54, 126, 139]. Experimental verification of dose response confirmed that low concentrations of volatile materials extracted from species-specific larval habitat materials increased oviposition, while there was a shift to reduced oviposition at high volatile concen‐ trations. Rejmankova et al [139] also confirmed through reciprocal treatment tests that volatile effect was strongly habitat/species-specific.

> Different mosquito species may rely on distinct chemical cues to avoid predators [187]. Mosquitoes that can detect aquatic predators often do so by sensing predator-released kairomones [187], see also review in Vonesh and Blaustein [188]. This was confirmed by preferential oviposition of *An. gambiae* in containers with clean water rather than water conditioned with predators (backswimmers, *Notonecta* sp. and tadpoles, *Xenopus* sp.) [72]. The experiment with Notonecta was later successfully repeated on other strains of *An. gambiae* by Warburg et al [187].

> After oviposition, the main factors determining larval survival are food availability and refuge from predators. Orr and Resh [66] documented microhabitat selection by larvae of *An. freeborni*. They found that larval distribution throughout the habitat (an emergent macrophyte, *Myriophyllum aquaticum*) was not random, but that the larvae tended to congregate in denser patches of macrophytes. Observational data confirmed an active mechanism of selection, i.e., larvae actively choose patches with higher plant densities.

> Larval habitats of the main malaria vectors in Belize are associated with three distinctly different aquatic environments: marshes with sparse macrophytes and cyanobacterial mats (*An. albimanus*), tall dense macrophyte marshes (*An. vestitipennis*), and floating detritus assemblages within freshwater rivers (*An. darlingi*). To assess species specific habitat suitabil‐ ity, we conducted mosquito transplant experiments [74]. First instar larvae of *An. albimanus, An*. *vestitipenis* and *An. darlingi* were placed in floating containers in the respective habitats of each species. Response of mosquito species to environmental conditions of its own and transplanted habitats clearly showed that each species was performing best in its own habitat. Survivorship of *An*. *vestitipenis* and *An. darlingi* in the *An. albimanus* habitat was extremely low or none.

## **5. Landscape context, remote sensing, GIS**

Larval habitats are not located in a vacuum, they are an integral part of a broader landscape and their environmental requirements should be studied in this context. The landscape level approach gained momentum when technologies such as remote sensing (RS) and GIS became widely used in 1990's [55, 57, 59, 60, 189-191] and it has continued improving with the progress in RS technology (see review in Machault and coauthors [192, 193]. Direct measurements of the Earth's hydrological and biophysical characteristics, its geological features and its climate from space have provided new data layers with spatial and temporal resolutions relevant to landscape-scale habitat characteristics and ecological processes [194, 195]. The landscape, vegetation, and ecosystem attributes derived from the applied remote sensing data contribute significantly to defining habitat characteristics and help discern patterns and gradients that may exist even within seemingly homogeneous environments.

The use of RS may involve various degree of complexity. The simplest case is when larval habitats are large enough to be directly identified within spatial resolution of remote sensors as, e.g., in Wood et al [55] study from irrigated rice in northern and central California. This study [55] provided a model of rice field mosquito population dynamics using spectral and spatial information. Analysis of field data revealed that rice fields with rapid early season vegetation canopy development, located near livestock pastures (i.e. bloodmeal sources), had greater mosquito larval populations than fields with more slowly developing vegetation canopies located further from pastures. Remote sensing reflectance measurements of early season rice canopy development and GIS measurements of distance to livestock pasture were combined to distinguish between high and low mosquito-producing rice fields. These distinctions were made with 90% accuracy nearly two months before anopheline larval populations peaked.

A more complex approach is needed in situations where larval habitats are spatially below the detection limit of RS data. As an example, a hierarchical approach was used to link larval habitat-types with larger land cover units in an integrated RS, GIS and field study in the Pacific coastal plain of Chiapas, Mexico [57]. Using this approach, villages with high *vs*. low risk for malaria transmission were identified and it was demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area [59, 60]. Similarly, RS generated maps of larval habitats in Madagascar rice fields and urban areas were used for predictions of adult densities and definitions of areas that may require indoor insecticide spraying [196, 197]. The landscape determinants of anopheline mosquito larval habitats in Kenya highlands and lowlands and their temporal changes were assessed by Mushinzimana et al. [198], Jacob et al [199], Munga et al [200], Mutuku et al [201], from elsewhere in Central and west Africa by Dambach [193] and Clennon et al [202], and from Malaysia by Ahmad et al [203]. The use of RS as a predictive tool to locate larval habitats has not always been successful as demonstrated by Achee et al [204]. Their results indicated that remotely sensed land cover is not a valuable indicator of the location in which *An. darlingi* larval habitats will form. High-resolution satellite imagery could be used to detect homes along river systems and potentially predict general areas at risk for *An. darlingi* breeding habitat formation based on distances from houses to waterways (Figure 3). The basic idea behind the remotely sensed assessment of larval habitats is to define environmental parameters that can be used to identify areas with increased risk of malaria transmission [193]. Yet, as already stated by

Roberts et al [205], the successful use of RS and GIS technologies to predict potential or actual malaria trouble spots is dependent on clear understandings of environmental factors that determine the presence of malaria vectors.

**Figure 3.** IKONOS 1m-resolution panchromatic image showing three houses (A-C) along a section of the Sibun River. Distance from the river to houses (black lines) was predictive for presence and abundance of *An. darlingi*, the primary malaria vector in Belize.

#### **5.1. Ecological niche models**

approach gained momentum when technologies such as remote sensing (RS) and GIS became widely used in 1990's [55, 57, 59, 60, 189-191] and it has continued improving with the progress in RS technology (see review in Machault and coauthors [192, 193]. Direct measurements of the Earth's hydrological and biophysical characteristics, its geological features and its climate from space have provided new data layers with spatial and temporal resolutions relevant to landscape-scale habitat characteristics and ecological processes [194, 195]. The landscape, vegetation, and ecosystem attributes derived from the applied remote sensing data contribute significantly to defining habitat characteristics and help discern patterns and gradients that

The use of RS may involve various degree of complexity. The simplest case is when larval habitats are large enough to be directly identified within spatial resolution of remote sensors as, e.g., in Wood et al [55] study from irrigated rice in northern and central California. This study [55] provided a model of rice field mosquito population dynamics using spectral and spatial information. Analysis of field data revealed that rice fields with rapid early season vegetation canopy development, located near livestock pastures (i.e. bloodmeal sources), had greater mosquito larval populations than fields with more slowly developing vegetation canopies located further from pastures. Remote sensing reflectance measurements of early season rice canopy development and GIS measurements of distance to livestock pasture were combined to distinguish between high and low mosquito-producing rice fields. These distinctions were made with 90% accuracy nearly two months before anopheline larval

A more complex approach is needed in situations where larval habitats are spatially below the detection limit of RS data. As an example, a hierarchical approach was used to link larval habitat-types with larger land cover units in an integrated RS, GIS and field study in the Pacific coastal plain of Chiapas, Mexico [57]. Using this approach, villages with high *vs*. low risk for malaria transmission were identified and it was demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area [59, 60]. Similarly, RS generated maps of larval habitats in Madagascar rice fields and urban areas were used for predictions of adult densities and definitions of areas that may require indoor insecticide spraying [196, 197]. The landscape determinants of anopheline mosquito larval habitats in Kenya highlands and lowlands and their temporal changes were assessed by Mushinzimana et al. [198], Jacob et al [199], Munga et al [200], Mutuku et al [201], from elsewhere in Central and west Africa by Dambach [193] and Clennon et al [202], and from Malaysia by Ahmad et al [203]. The use of RS as a predictive tool to locate larval habitats has not always been successful as demonstrated by Achee et al [204]. Their results indicated that remotely sensed land cover is not a valuable indicator of the location in which *An. darlingi* larval habitats will form. High-resolution satellite imagery could be used to detect homes along river systems and potentially predict general areas at risk for *An. darlingi* breeding habitat formation based on distances from houses to waterways (Figure 3). The basic idea behind the remotely sensed assessment of larval habitats is to define environmental parameters that can be used to identify areas with increased risk of malaria transmission [193]. Yet, as already

may exist even within seemingly homogeneous environments.

414 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

populations peaked.

stated by

Populations of mosquito larvae are ideally suited to GIS and remote sensing applications due to their close association with their microenvironment. Specifically, larval mosquitoes have three distinct ecological characteristics that are directly related to predictive risk-modeling: 1) specific habitat preferences, 2) microclimate requirements and 3) vegetation-dependent associations to include plant height and density. Spatial-temporal interactions of mosquito larvae with their natural environment are critical to understanding the risk of contact between the vectors and their human hosts. Due to the fact that mosquitoes spend a substantial portion of their life cycle in the larval stage, population structure and vector survival is greatly influenced by the environmental surroundings. One area that is increasingly being applied to disease ecology which takes advantage of these environmental associations is the use ecolog‐ ical niche models [206]. An ecological niche model is an estimate of the distribution of a species and requires two input data sets: the known locations of a species and environmental data in an image format (such as larval habitats, climate data, elevation data, land cover, etc.). The ecological niche modeling program examines the environmental data at the locations where the species occurs to infer the environmental requirements of the species across a much larger area. The requirements of the species are then used to create a map of the predicted distribution of the species. Any species affected by environmental conditions such as climate can be modeled including disease vectors, disease hosts and pathogens. Models of monthly predic‐ tions of dengue fever in Mexico have been created based on mosquito activity [207]. Niche models of malaria vectors in the *An. gambiae* complex have been developed for under-sampled regions of Africa [208]. The benefit of niche modeling is the development of maps showing predicted distribution of an organism based on current and projected vector ecology and environmental data.

## **6.  Population pressure results in transformation of natural ecosystems to agriculture, construc‐ tion of roads and hydroelectric dams, irrigation projects, open pit mines, and uncontrolled human colonization [209, 210]. Anthropogenic modification of the ecosystems also contributes to global climate change represented by an increase in temperature and accompanied by extremes of the hydrologic cycle (e.g., floods and droughts) [211, 212]. The global rate of tropical deforestation continues with nearly 2% to 3% of global forests lost each year and land use change for agriculture represents the largest driver of land cover change across the earth [85, 209, 213]. Arthropod vectors in general, and insect vectors in particular are very sensitive to their environment, which determines their presence, development and behavior. As a consequence, climatic, as well as landscape and land cover factors greatly influence the spatial distribution of vectors and the diseases they transmit [214].

Mosquitoes are among the most sensitive insects to environmental change; their survival, density, and distribution are dramatically influenced by small changes in environmental conditions, such as temperature, humidity, and the availability of suitable larval habitats [48, 88, 215-219]. All these changes can alter the incidence, seasonality and intensity of transmis‐ sion, and geographic range of diseases such as malaria. Changes in the distribution of malaria cases and intensities of malaria transmission have been documented by many historical examples. As described by Hackett [220], malaria increased in Malaya as jungle was cleared for rubber plantations. Where forest was removed the sun penetrated and populations of *Anopheles maculatus* mosquitoes proliferated, greatly increasing the incidence of human malaria. The better we are able to assess and explain the distribution and dynamics of vector species in relation to fluctuations in their environments, the more accurate prediction can be made of malaria in the context of ongoing environmental change [221, 222]. This will allow us to evaluate the risks associated with current practices, better explain the patterns of increasing and decreasing disease, better identify measures to mitigate the likelihood and impact of disease emergence, and eventually improve its control [213]. Below are specific examples of changes related to important human activities.

#### **6.1. Deforestation**

the species occurs to infer the environmental requirements of the species across a much larger area. The requirements of the species are then used to create a map of the predicted distribution of the species. Any species affected by environmental conditions such as climate can be modeled including disease vectors, disease hosts and pathogens. Models of monthly predic‐ tions of dengue fever in Mexico have been created based on mosquito activity [207]. Niche models of malaria vectors in the *An. gambiae* complex have been developed for under-sampled regions of Africa [208]. The benefit of niche modeling is the development of maps showing predicted distribution of an organism based on current and projected vector ecology and

Natural ecosystems throughout the world are being severely altered by human intervention. Population pressure results in transformation of natural ecosystems to agriculture, construc‐ tion of roads and hydroelectric dams, irrigation projects, open pit mines, and uncontrolled human colonization [209, 210]. Anthropogenic modification of the ecosystems also contributes to global climate change represented by an increase in temperature and accompanied by extremes of the hydrologic cycle (e.g., floods and droughts) [211, 212]. The global rate of tropical deforestation continues with nearly 2% to 3% of global forests lost each year and land use change for agriculture represents the largest driver of land cover change across the earth [85, 209, 213]. Arthropod vectors in general, and insect vectors in particular are very sensitive to their environment, which determines their presence, development and behavior. As a consequence, climatic, as well as landscape and land cover factors greatly influence the spatial

Mosquitoes are among the most sensitive insects to environmental change; their survival, density, and distribution are dramatically influenced by small changes in environmental conditions, such as temperature, humidity, and the availability of suitable larval habitats [48, 88, 215-219]. All these changes can alter the incidence, seasonality and intensity of transmis‐ sion, and geographic range of diseases such as malaria. Changes in the distribution of malaria cases and intensities of malaria transmission have been documented by many historical examples. As described by Hackett [220], malaria increased in Malaya as jungle was cleared for rubber plantations. Where forest was removed the sun penetrated and populations of *Anopheles maculatus* mosquitoes proliferated, greatly increasing the incidence of human malaria. The better we are able to assess and explain the distribution and dynamics of vector species in relation to fluctuations in their environments, the more accurate prediction can be made of malaria in the context of ongoing environmental change [221, 222]. This will allow us to evaluate the risks associated with current practices, better explain the patterns of increasing and decreasing disease, better identify measures to mitigate the likelihood and impact of disease emergence, and eventually improve its control [213]. Below are specific examples of

environmental data.

**6. Human impact land use/global change**

416 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

distribution of vectors and the diseases they transmit [214].

changes related to important human activities.

Deforestation is one of the most important factors driving emerging and re-emerging infectious diseases. Through the process of clearing forests and subsequent agricultural development, deforestation changes almost every attribute of local ecosystems such as microclimate, soil, and aquatic conditions, and most significantly, the ecology of local flora and fauna, including human disease vectors. Numerous country and area studies have described the influence of deforestation and subsequent land use on the density of local mosquito vectors [223]. One of the most thorough evaluations of the impact of deforestation combined with the prediction of future changes has been presented by Yasuoka and Levins [224] who conducted a metaanalysis of 60 published studies of changes in ecology of 31 anopheline species and malaria incidence as a consequence of deforestation. In comprehensive tables they summarized density changes by land cover, and for larval habitats the niche-width and sun-preference indices of each species. The conclusion was 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 species were directly affected by deforestation, some favored or could adapt to the different environmental conditions, and some invaded and/or replaced other species in the process of development and cultivation. 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 become exposed to sunlight.

As a specific example of the complexity of a malaria vector to deforestation we present the case of *An. darlingi* in the Amazon region. Vittor et al [225] examined the larval breeding habitat of a major South American malaria vector, *An. darlingi,* in areas with varying degrees of ecologic alteration in the Peruvian Amazon and concluded that deforestation and associated ecologic alterations are conducive to *An. darlingi* larval presence, and thereby increase malaria risk. According to Barros et al [82], deforestation and human presence creates a new habitat, a forest fringe ecosystem, by promoting three changes in *An. darlingi* bionomics: (i) increasing contact with humans; (ii) increasing the number of microdams (small river obstruction causing the accumulation of debris), which increases the number of potential larval habitats as well as the breeding season; and (iii) reducing the number of shaded breeding sites in a given geographical area, which results in a concentration of larvae in remaining shaded areas. The ideal breeding site occurs in the forest fringe, where the three factors, shade, microdams and human blood meals, are located close to each other.

Environmental changes caused by deforestation often lead to vector replacement (for examples referenced in older papers see Service [136]). Conn et al. [226] conducted entomological surveys in malaria areas of Macapá, northeastern Amazonia, and found *An. marajoara* replacing *An. darlingi* as the primary vector. It is hypothesized that the observed change in mosquito population densities was caused by deforestation for agriculture that resulted in newly created ground pools favoring *An. marajoara* larvae. For many regions in the Amazon Basin, popula‐ tions of *An. darlingi* have increased because road construction in the forest has considerably expanded the breeding sites—large areas of neutral, partially shaded and unpolluted water. These characteristics also attract human inhabitants. Subsequently, clearing of forests and water pollution reduce the suitability of these for *An. darlingi* breeding. However, these sites, and newly created ponds for agricultural use, attract other mosquito species such as *An. marajoara*. In addition, humans have colonized land near extensive marshy areas, another preferred breeding habitat of *An. marajoara*.

#### **6.2. Dam construction**

Water reservoirs have long been recognized to be a risk factor for malaria transmission [227-231]. Hydroelectric or irrigation dam construction increases the habitat availability by the formation of lakes. Shallow parts of these lakes are typically overgrown with macrophytes that provide excellent breeding sites for anopheline mosquitoes [227]. However, compared to the number of studies on land use change due to deforestation and agricultural expansion, research related to the entomological and ecological determinants of the rising malaria burden in the vicinity of large dams is rather limited [232]. There are historical examples, such as that of Tennessee Valley Authority ([35], see also p. 3) of well executed environmental management measures to control malaria vectors [21, 35]. These successfully executed environmental measures can be adapted to control malaria associated with dam construction in sub-Saharan Africa and elsewhere in malaria endemic regions. Construction of new reservoirs under the tropical, sub-humid climatic conditions should therefore be accompanied by entomologic studies to predict the risk of malaria epidemics [233]. Keiser et al [231] calls for institutionali‐ zation of health impact assessments for future water development projects analogous to environmental impact assessments as well as the employment of monitoring and surveillance systems that would facilitate systematic evaluation of the impact of these ecosystem interven‐ tions over time. The reality is that more dams will be built and thus mitigation strategies to alleviate potential negative health effects are mandatory to reduce the current burden of malaria in settings near irrigation or dam projects.

#### **6.3. Wetland destruction**

Draining wetlands has been extensively practiced and promoted as the easiest solution to localized public health threats posed by malaria vectors [21, 234]. Unfortunately, this practice has not always worked. Among many cases of increasing malaria transmission after destruc‐ tion of natural wetlands are the examples from African papyrus swamps [64]. As stated already by Goma [32, 235] and confirmed recently by others [72, 88, 236], the interior of a papyrus swamp is unsuitable for anophelines, while the swamp periphery and cultivation of natural swamps provides productive larval habitats for *An. gambiae* and consequently, increase the risks of malaria transmission to the human population. Many natural wetlands have been destroyed and changed to brick-making pits – the most abundant habitat type containing *An. gambiae* larvae in Africa [237].

What has not been taken into account when manipulating wetlands for health benefits is the loss of valuable ecosystem services provided by these wetlands, such as water purification, flood control, or provision of food and fiber, and their contributions to human health. This aspect was emphasized by the 2008 Conference of the Contracting Parties to the Ramsar Convention on Wetlands, whose resolution stated among others: "Those concerned with wetland conservation and management should encourage new and ongoing research regard‐ ing the links between wetlands and human health and to bring information on the scientifically proven contributions that functioning wetland ecosystems make to good health to the attention of national ministries and agencies responsible for health, sanitation, and water supply. The human health sector, and all relevant stakeholders should collaborate in assessing the consequences of wetland management linked with human health, and vice versa the conse‐ quences for the ecological character of wetlands of current practices which seek to maintain or improve human health, including the identification of appropriate trade-offs in decisionmaking."

#### **6.4. Wetland creation and restoration**

These characteristics also attract human inhabitants. Subsequently, clearing of forests and water pollution reduce the suitability of these for *An. darlingi* breeding. However, these sites, and newly created ponds for agricultural use, attract other mosquito species such as *An. marajoara*. In addition, humans have colonized land near extensive marshy areas, another

Water reservoirs have long been recognized to be a risk factor for malaria transmission [227-231]. Hydroelectric or irrigation dam construction increases the habitat availability by the formation of lakes. Shallow parts of these lakes are typically overgrown with macrophytes that provide excellent breeding sites for anopheline mosquitoes [227]. However, compared to the number of studies on land use change due to deforestation and agricultural expansion, research related to the entomological and ecological determinants of the rising malaria burden in the vicinity of large dams is rather limited [232]. There are historical examples, such as that of Tennessee Valley Authority ([35], see also p. 3) of well executed environmental management measures to control malaria vectors [21, 35]. These successfully executed environmental measures can be adapted to control malaria associated with dam construction in sub-Saharan Africa and elsewhere in malaria endemic regions. Construction of new reservoirs under the tropical, sub-humid climatic conditions should therefore be accompanied by entomologic studies to predict the risk of malaria epidemics [233]. Keiser et al [231] calls for institutionali‐ zation of health impact assessments for future water development projects analogous to environmental impact assessments as well as the employment of monitoring and surveillance systems that would facilitate systematic evaluation of the impact of these ecosystem interven‐ tions over time. The reality is that more dams will be built and thus mitigation strategies to alleviate potential negative health effects are mandatory to reduce the current burden of

Draining wetlands has been extensively practiced and promoted as the easiest solution to localized public health threats posed by malaria vectors [21, 234]. Unfortunately, this practice has not always worked. Among many cases of increasing malaria transmission after destruc‐ tion of natural wetlands are the examples from African papyrus swamps [64]. As stated already by Goma [32, 235] and confirmed recently by others [72, 88, 236], the interior of a papyrus swamp is unsuitable for anophelines, while the swamp periphery and cultivation of natural swamps provides productive larval habitats for *An. gambiae* and consequently, increase the risks of malaria transmission to the human population. Many natural wetlands have been destroyed and changed to brick-making pits – the most abundant habitat type containing *An.*

What has not been taken into account when manipulating wetlands for health benefits is the loss of valuable ecosystem services provided by these wetlands, such as water purification, flood control, or provision of food and fiber, and their contributions to human health. This aspect was emphasized by the 2008 Conference of the Contracting Parties to the Ramsar

preferred breeding habitat of *An. marajoara*.

418 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

malaria in settings near irrigation or dam projects.

**6.3. Wetland destruction**

*gambiae* larvae in Africa [237].

**6.2. Dam construction**

In addition to rice fields, which are the most extensive human made wetlands and their significance as larval habitat has been already described previously, the use of constructed wetlands for wastewater treatment is expanding [236, 238, 239]. Constructed wetland tech‐ nology has broad applications for the treatment of many types of wastewaters and provides an ecological approach to mitigate the release of nutrients and toxic materials into the envi‐ ronment [240]. However, design features, maintenance activities and the characteristics of the wastewater undergoing treatment contribute differentially to potential levels of mosquito production and, consequently, to threats to human and animal health from mosquito-borne pathogens. Nutrients (nitrogen and phosphorus), and the configuration and maintenance of emergent vegetation can have strong effects on mosquito production. As loading rates of organic matter and nutrients decline, the diversity of mosquitoes produced by treatment wetlands tends to increase and the relative abundance of *Anopheles* species increases in temperate man-made wetlands [239, 241]. A proper design, e.g. subsurface rather than surface flow or flow-through rather than pond-type wetland [242] can help local mosquito problems. Surface-flow wetlands can also be designed to minimize mosquito breeding by increasing macro-invertebrate predators [243]. Greenway [243] concluded that a marsh with a diversity of macrophytes appears optimal for macro-invertebrate biodiversity and the control of mosquito larvae by predation. The key to mosquito management is to ensure a well-balanced ecosystem supporting a diversity of aquatic organisms [240]. A general conclusion from those areas that contain both treatment wetlands and unimpacted natural wetlands is that ade‐ quately designed and appropriately managed treatment wetlands do not pose any greater mosquito threat than the existing natural wetlands [244].

To compensate for a large loss of wetlands in the past, we are now witnessing many projects attempting to restore, rehabilitate, or create various types of wetland habitats. The resulting restored wetland areas provide flood control, improve water quality, and provide habitat for wildlife, especially bird species. However, they create great mosquito habitat and only a few restoration project address this issue properly [234] and there is a need for a better coordination between wetland restoration design and management and mosquito larval management.

#### **6.5. Eutrophication**

Freshwaters are among the most extensively and rapidly altered ecosystems on the planet [213]. Increased use of fertilizers in agriculture and destruction of natural buffer zones leads to runoff of excessive nutrients, specifically nitrogen and phosphorus to lakes, rivers and reservoirs [245-250]. Nutrient increase is generally responsible for plant production resulting in potential changes in other trophic levels. Several studies have shown positive correlations between concentrations of inorganic nutrients in surface waters and larval abundance for *Anopheles* [43, 251]. Nutrient enriched waters are easily invaded by aggressive aquatic weeds such as water hyacinth (*Eichhornia crassipes*), which are known to be very productive anophe‐ line habitats [37, 44, 252].

The authors' research in Belize [56, 86] provided data in support of the hypothesis that eutrophication causes changes in freshwater communities. The Central American country of Belize contains large wetland areas that used to be dominated by phosphorus limited sparse macrophyte communities interspersed with floating mats of cyanobacteria – a typical *An. albimanus* habitat (Figure 4).

**Figure 4.** Schematic representation of the change of plant communities in marshes of Belize caused by increased eu‐ trophication by phosphorus. This change is accompanied by the replacement of *An. albimanus* habitat with An. vestiti‐ *pennis* habitat.

Anthropogenically mediated P enrichment of wetland plant communities through introduc‐ tion of fertilizer runoff from expanding sugar cane fields is causing a switch from sparse macrophytes to tall dense macrophytes represented mostly by *Typha domingensis*. Tall dense macrophytes provide favorable habitat for *An. vestitipennis*, which appears to be a more efficient vector of malaria. Thus human-caused nutrient enrichment of marshes may lead to increased risk of malaria transmission in human settlements in proximity to the impacted marshes.

#### **6.6. Temperature and precipitation changes**

Malaria transmission is very sensitive to both temperature and precipitation, which makes the issue of change in risk due to past and projected warming trends one of the most important climate change-health questions to follow [253, 254]. Large malaria epidemics in the East African highlands during the mid and late 1990s initiated research on the role that global warming might have on malaria transmission. Historically, these highlands have been used as a shelter against malaria because malaria has been naturally absent due to conditions that limit the biology of the parasite [255]. Several authors proposed that spread of malaria into areas that rarely saw malaria transmission could be related to the impacts of small increases in temperature [253, 256]. The issue became hotly debated [255]. Recently, Chaves et al [257] assessed conclusions from both sides of the argument and found that evidence for the role of climate is robust but they also found a large heterogeneity in malaria trends.  The global change is expected to influence rainfall patterns both seasonal rainfall totals and inter-annual variability in malaria endemic regions, and these events will impact larval habitats availability and thus mosquito population dynamics [258].

#### **6.7. Sea level rise**

**6.5. Eutrophication**

420 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

line habitats [37, 44, 252].

*albimanus* habitat (Figure 4).

*pennis* habitat.

Freshwaters are among the most extensively and rapidly altered ecosystems on the planet [213]. Increased use of fertilizers in agriculture and destruction of natural buffer zones leads to runoff of excessive nutrients, specifically nitrogen and phosphorus to lakes, rivers and reservoirs [245-250]. Nutrient increase is generally responsible for plant production resulting in potential changes in other trophic levels. Several studies have shown positive correlations between concentrations of inorganic nutrients in surface waters and larval abundance for *Anopheles* [43, 251]. Nutrient enriched waters are easily invaded by aggressive aquatic weeds such as water hyacinth (*Eichhornia crassipes*), which are known to be very productive anophe‐

The authors' research in Belize [56, 86] provided data in support of the hypothesis that eutrophication causes changes in freshwater communities. The Central American country of Belize contains large wetland areas that used to be dominated by phosphorus limited sparse macrophyte communities interspersed with floating mats of cyanobacteria – a typical *An.*

**Figure 4.** Schematic representation of the change of plant communities in marshes of Belize caused by increased eu‐ trophication by phosphorus. This change is accompanied by the replacement of *An. albimanus* habitat with An. vestiti‐

Along with warming temperatures, any increase in sea levels will affect the extent of saline (>30 ppt) or brackish (0.5-30 ppt) water bodies in coastal areas. These include coastal estuaries, lagoons, marshes and mangroves [106]. An expansion of brackish and saline water bodies in coastal areas, associated with rising sea levels, can increase densities of salinity-tolerant vector mosquitoes and lead to the adaptation of freshwater vectors to breed in brackish and saline waters. Higher vector densities can increase transmission of vector-borne infectious diseases in coastal localities, which can then spread to other areas [106].

The consequences of human-induced ecological changes provide another set of examples. Large-scale shrimp farming in the Mekong delta of Vietnam locally increased the density of *An. sundaicus*[259]. The greater availability of brackish water bodies can also lead to freshwater breeding mosquitoes such as *An. stephensi* and *An. culicifacies* getting adapted to breed in brackish waters as was observed immediately after the 2004 tsunami in India [260] and some years later in eastern Sri Lanka [261].

#### **6.8. Replacability and adaptability**

As already indicated by a few examples in the above text, a change in ecology of a region whether due natural factors or human impact can lead to changes in the quality and quantity of larval habitats. This often leads to changes in mosquito population dynamics and species composition [262]. The original anopheline species can be replaced by species better adapted to new conditions or they can adapt themselves. Mosquito species distributed over broad geographic ranges are more likely to have greater habitat diversity than species distributed over a small range [263] and thus their adaptability can be higher. Except for a few examples, our knowledge on the species adaptability is quite limited. But since at least some species are able to adapt to different environmental conditions, an effort needs to be made to obtain data on anopheline population dynamics before, during, and after ecologic alterations. Further‐ more, the long-term effectiveness of any control strategy will depend on whether vectors respond to the evolutionary selection pressure created by intervention [22]. For example, mosquitoes may respond by phenotypic plasticity, or by evolving traits such as insecticide resistance or behavioral avoidance.

### **7. Implication for vector control**

Malaria vector control targeting the larval stages of mosquitoes was applied successfully against many species of *Anopheles* in malarious countries until the mid-20th Century [3, 8, 264-266]. Since the introduction of DDT in the 1940s and the associated development of indoor residual spraying (IRS), which usually has a more powerful impact on vectorial capacity than larval control, the focus of malaria prevention programs shifted to the control of adult vectors [8, 267]. However, when it became clear that this strategy is not working (Service 1983), an integrated disease management approach including control of larval stages of malaria vectors, i.e., Integrated Vector Management (IVM) began to be reconsidered [21, 268]. A great step in that direction was made by Keiser et al [264] who provided a systematic review and a metaanalysis of malaria control programs, emphasizing environmental management as their main feature. Most of the 40 studies (85%) were implemented before the Global Malaria Eradication Campaign (1955–69). The authors concluded that malaria control programs that emphasize environmental management are highly effective in reducing malaria. Lessons learned from these past successful programs can guide sound and sustainable malaria control approaches and strategies. The conclusions of Keiser's et al [231] meta-analysis of past control strategies are in agreement with recently developed malaria transmission models showing that sub‐ stantial reductions of the entomological inoculation rate are possible when an integrated malaria control program with multiple interventions (e.g., environmental management tools) implemented simultaneously is used [269, 270].

The larval source management (LSM) also termed Environmental management that has been successfully used to control mosquitoes in many developed countries (US, Brazil, Canada) is recently becoming an integral component of malaria control methods in Africa [271]. LSM includes: (1) habitat (or environmental) modification, (2) habitat (environmental) manipula‐ tion, (3) biological control and (4) larviciding [236, 264, 271]. **Habitat modification** is designed to prevent, eliminate, or reduce vector habitat and it involves a permanent change of land and water, including landscaping, drainage of surface water, land reclamation and filling but also coverage of large water storage containers, wells and other potential breeding sites. **Habitat manipulation** refers to activities that reduce larval habitats of the vector mosquito through temporary changes to the aquatic environment in which larvae develop. It is a recurrent activity, such as water-level manipulation, which includes measures such as flushing, drain clearance, shading or exposing habitats to the sun depending on the ecology of the local vector. It may include planting water-intensive tree species such as *Eucalyptus robusta* to reduce standing water in marshy areas. The best strategies are those that are adapted to local vector ecology, epidemiology and resources, guided by operational research and subject to routine monitoring and evaluation [22, 272]. Bond's et al [122, 123] studies can serve as an example of habitat manipulation. They report on how manual algal removal from breeding pools along a river in southern Mexico significantly reduced both larval and adult densities of *An. pseudo‐ punctipennis*. In a follow up study, the abundance of *An. pseudopunctipennis* larvae + pupae was dramatically reduced by this treatment and remained depressed for two to three months. Algal extraction did not reduce the overall abundance of aquatic insects in river pools. **Biological control** of mosquitoes refers to the introduction of natural enemies into aquatic habitats; these are predatory fish or invertebrates, parasites or disease organisms (see the predator section). *Bacillus thuringiensis israelensis* (Bti) and *Bacillus sphaericus* (Bs) are bacterial species reported to be effective against mosquitoes, and have been widely studied and used as biolarvicides [266, 273, 274]. Recently, researchers have focused on the resident microbiota of insect vectors that can potentially impede transmission of human pathogens. These microbes may prove effective agents for manipulating the vector competence of malaria and other important human pathogens [275-278]. Biological control agents should be evaluated with respect to their climatic compatibility and their capability to maintain very close interactions with target populations [155].

## **8. What next?**

**6.8. Replacability and adaptability**

422 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

resistance or behavioral avoidance.

**7. Implication for vector control**

implemented simultaneously is used [269, 270].

As already indicated by a few examples in the above text, a change in ecology of a region whether due natural factors or human impact can lead to changes in the quality and quantity of larval habitats. This often leads to changes in mosquito population dynamics and species composition [262]. The original anopheline species can be replaced by species better adapted to new conditions or they can adapt themselves. Mosquito species distributed over broad geographic ranges are more likely to have greater habitat diversity than species distributed over a small range [263] and thus their adaptability can be higher. Except for a few examples, our knowledge on the species adaptability is quite limited. But since at least some species are able to adapt to different environmental conditions, an effort needs to be made to obtain data on anopheline population dynamics before, during, and after ecologic alterations. Further‐ more, the long-term effectiveness of any control strategy will depend on whether vectors respond to the evolutionary selection pressure created by intervention [22]. For example, mosquitoes may respond by phenotypic plasticity, or by evolving traits such as insecticide

Malaria vector control targeting the larval stages of mosquitoes was applied successfully against many species of *Anopheles* in malarious countries until the mid-20th Century [3, 8, 264-266]. Since the introduction of DDT in the 1940s and the associated development of indoor residual spraying (IRS), which usually has a more powerful impact on vectorial capacity than larval control, the focus of malaria prevention programs shifted to the control of adult vectors [8, 267]. However, when it became clear that this strategy is not working (Service 1983), an integrated disease management approach including control of larval stages of malaria vectors, i.e., Integrated Vector Management (IVM) began to be reconsidered [21, 268]. A great step in that direction was made by Keiser et al [264] who provided a systematic review and a metaanalysis of malaria control programs, emphasizing environmental management as their main feature. Most of the 40 studies (85%) were implemented before the Global Malaria Eradication Campaign (1955–69). The authors concluded that malaria control programs that emphasize environmental management are highly effective in reducing malaria. Lessons learned from these past successful programs can guide sound and sustainable malaria control approaches and strategies. The conclusions of Keiser's et al [231] meta-analysis of past control strategies are in agreement with recently developed malaria transmission models showing that sub‐ stantial reductions of the entomological inoculation rate are possible when an integrated malaria control program with multiple interventions (e.g., environmental management tools)

The larval source management (LSM) also termed Environmental management that has been successfully used to control mosquitoes in many developed countries (US, Brazil, Canada) is recently becoming an integral component of malaria control methods in Africa [271]. LSM includes: (1) habitat (or environmental) modification, (2) habitat (environmental) manipula‐ tion, (3) biological control and (4) larviciding [236, 264, 271]. **Habitat modification** is designed Almost every paper that we reviewed for this chapter ends up with the call for more infor‐ mation on larval stages of malaria vectors, in order to enable a better vector control and more accurate predictions of vector response to changing environment. It is (finally!) becoming clear that understanding the ecology and evolution of mosquito vectors needs to complement epidemiology, genetics and molecular biology in solving malaria problems. Several review papers provide good suggestions for future directions in vector ecology research (see, e.g., Table 2 in Chaves and Koenraadt [255] and Box 3 in Ferguson et al [22]). As stated in the preceding text, almost any factor defining a larval habitat can change as a result of direct human modification (deforestation, agricultural practices, eutrophication) and/or indirectly caused environmental change (temperature, precipitation). In addition, new habitats can be created. All these changes can and will impact the basic environmental determinants of larval habitats – food availability, refuge, predator presence. There are indications that some species will be able to adapt, some will be replaced by other species, and some anophelines that have not traditionally been regarded as vectors may become important ones.

In the context of ecosystem change whether due to nutrient, temperature, precipitation, salinity or vegetation changes, there is a strong need for studies on adaptability of different anopheline species to new conditions. The majority of these studies would be best executed as manipula‐ tive field or semi-field experiments focused not only on changing characteristics of species performance but also on interactions with other species (both competition and predation). To be able to accomplish these types of experiments, systems of enclosed, pathogen-free, semifield mesocosms in which vector populations can be experimentally manipulated will have to be established within environmentally realistic, contained semi-field settings. See, e.g., Ng'habi et al [11] semi-field system of large, netting-enclosed mesocosms, in which vectors can fly freely, feed on natural plant and vertebrate host sources, and access realistic resting and oviposition sites. Ideally, systems of these experimental mesocosms should be established along environmental (temperature, precipitation) gradients or with the capability to experi‐ mentally manipulate these variables so that we can conduct the experiments focused on species response to changing environments.

In addition, there is an ongoing need for regular monitoring and good quality long-term dataset on species distributions. High resolution satellite data enable more detailed observa‐ tions on vegetation changes and regional distribution of precipitations and temperature, which all can results and result in better risk prediction maps [193]. In order to include a temporal component to the risk models, a network of longitudinal population monitoring sites for vector development needs to be established. The ecological niche models [206, 279] mentioned above will undoubtedly play increasingly important role in predictions of disease outbreaks.

## **Acknowledgements**

We thank Stephanie Castle for technical help. Parts of the research referred to in the chapter was supported by the NIH-NSF Ecology of Infectious Diseases program, Grant # R01 AI49726, "Environmental Determinants of Malaria in Belize".

## **Author details**

Eliška Rejmánková1\*, John Grieco2 , Nicole Achee2 and Donald R. Roberts2,3

\*Address all correspondence to<EMAIL_ADDRESS>
1 Department of Environmental Science and Policy, University of California, Davis, USA

2 Department of Preventive Medicine & Biometrics, Uniformed Services University of the Health Sciences, Bethesda, USA

3 Retired, Professor Emeritus, present address: Clifton Forge, VA, USA

## **References**

In the context of ecosystem change whether due to nutrient, temperature, precipitation, salinity or vegetation changes, there is a strong need for studies on adaptability of different anopheline species to new conditions. The majority of these studies would be best executed as manipula‐ tive field or semi-field experiments focused not only on changing characteristics of species performance but also on interactions with other species (both competition and predation). To be able to accomplish these types of experiments, systems of enclosed, pathogen-free, semifield mesocosms in which vector populations can be experimentally manipulated will have to be established within environmentally realistic, contained semi-field settings. See, e.g., Ng'habi et al [11] semi-field system of large, netting-enclosed mesocosms, in which vectors can fly freely, feed on natural plant and vertebrate host sources, and access realistic resting and oviposition sites. Ideally, systems of these experimental mesocosms should be established along environmental (temperature, precipitation) gradients or with the capability to experi‐ mentally manipulate these variables so that we can conduct the experiments focused on species

In addition, there is an ongoing need for regular monitoring and good quality long-term dataset on species distributions. High resolution satellite data enable more detailed observa‐ tions on vegetation changes and regional distribution of precipitations and temperature, which all can results and result in better risk prediction maps [193]. In order to include a temporal component to the risk models, a network of longitudinal population monitoring sites for vector development needs to be established. The ecological niche models [206, 279] mentioned above will undoubtedly play increasingly important role in predictions of disease outbreaks.

We thank Stephanie Castle for technical help. Parts of the research referred to in the chapter was supported by the NIH-NSF Ecology of Infectious Diseases program, Grant # R01 AI49726,

, Nicole Achee2

1 Department of Environmental Science and Policy, University of California, Davis, USA

2 Department of Preventive Medicine & Biometrics, Uniformed Services University of the

and Donald R. Roberts2,3

response to changing environments.

424 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**Acknowledgements**

**Author details**

Eliška Rejmánková1\*, John Grieco2

Health Sciences, Bethesda, USA

"Environmental Determinants of Malaria in Belize".

\*Address all correspondence to<EMAIL_ADDRESS>
3 Retired, Professor Emeritus, present address: Clifton Forge, VA, USA

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## **From** *Anopheles* **to Spatial Surveillance: A Roadmap Through a Multidisciplinary Challenge**

Valérie Obsomer, Nicolas Titeux, Christelle Vancustem, Grégory Duveiller, Jean-François Pekel, Steve Connor, Pietro Ceccato and Marc Coosemans

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55622

**1. Introduction**

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[275] Favia G, Ricci I, Damiani C, Raddadi N, Crotti E, Marzorati M, et al. Bacteria of the genus Asaia stably associate with *Anopheles stephensi*, an Asian malarial mosquito vector. Proceedings of the National Academy of Sciences of the United States of

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*Anopheles* mosquito species are diverse and vector of many pathogens. A review of the genus *Anopheles* [1] recently updated [2] listed more than 520 species, some of which including subspecies and cryptic species. Each of them presents ecologic requirements and behaviours that can influence their status as vector for specific pathogens. Pathogen transmission dynam‐ ics vary greatly from one region to another such as documented for the biodiversity of malaria in the world [3]. Acknowledging these variations at local scale within a country through detailed mapping can lead to better targeted measures and improved monitoring. Interactions between vectors, pathogens and humans in a given area can be better comprehended using a spatial framework leading to what we call here a spatial surveillance.

Part of spatial variation is explained by differences in pathogen species or by successful control in some areas. Nevertheless, *Anopheles* species play a major part in the occurrence, seasonality and spatial variation of *Anopheles*-borne diseases. The environment in a given region provides or not support for a given species to breed, thrive and live long enough to be an efficient vector. Because of these variations a species might be an efficient vector in one settlement and then only a secondary vector in another. The need to clarify *Anopheles* distribution is recognised as a crucial step towards malaria eradication [4]. A recent effort to provide detailed maps of malaria and vectors has been carried out [5–8] including a description of ecological require‐ ments. While these distribution maps are essential for an overview, some issues [9] (described further) linked to the data and modelling impede usage in an operational world. Modelling

© 2013 Obsomer et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Obsomer et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

and surveillance are key activities for successful control but integration of all components for effective operational surveillance is not straightforward.

In this chapter we review the challenges posed by spatial surveillance of *Anopheles*-borne diseases with particular attention for malaria surveillance. This challenge will mainly reside in the difficulty of getting the appropriate raw data and the large spectrum of multidisciplinary expertise. We propose here a roadmap from *Anopheles* sampling to a spatial surveillance.

## **2. Problem statement and application area**

When working on vector-borne-diseases, decision makers and researchers often face a lack of specific quality data required for optimal targeting the intervention and surveillance. How‐ ever, the results/decisions are critical as they impact on the lives of many people. Numerous studies use suboptimal vector dataset, and proxies for environmental drivers to map vector distribution or provide basis for vector surveillance. However, the uncertainties linked to the original dataset are not always well documented, in particular regarding proxies for environ‐ mental drivers derived from satellite imagery. Analysis methods also do not always take into account the specificity of species ecological distribution and inaccuracies linked to the vector dataset.

## **3. Research course and method used**

Spatial surveillance of vector-borne diseases should integrate specialised knowledge in entomology, ecology, parasitology, epidemiology, human health, ecological modelling and social sciences. The authors of this chapter are specialized in those different fields and teamed up to offer an overview of the challenges posed by spatial surveillance. As vector-bornediseases are linked to the environment, a spatial analysis using geographical information or remote sensing related technologies seems then appropriate.

### **4. Following the road map**

Reliable outputs to go from *Anopheles* to spatial surveillance first depend on the data entering any analysis or decision process, being the data on *Anopheles* or on environmental factors. Environmental factors provided by remote sensing techniques that could be used to predict distribution or occurrence of malaria and *Anopheles* have already been reviewed [10]. Based on this inventory, we analyse pros and cons of *Anopheles* sampling strategies, various types of data and modelling techniques. Finally, useful initiatives to make research efforts available and operational in the field are discussed. The general scheme is provided in figure 1.

**Figure 1.** General scheme of the roadmap for *Anopheles* spatial surveillance

#### **4.1.** *Anopheles* **and sampling strategies**

and surveillance are key activities for successful control but integration of all components for

In this chapter we review the challenges posed by spatial surveillance of *Anopheles*-borne diseases with particular attention for malaria surveillance. This challenge will mainly reside in the difficulty of getting the appropriate raw data and the large spectrum of multidisciplinary expertise. We propose here a roadmap from *Anopheles* sampling to a spatial surveillance.

When working on vector-borne-diseases, decision makers and researchers often face a lack of specific quality data required for optimal targeting the intervention and surveillance. How‐ ever, the results/decisions are critical as they impact on the lives of many people. Numerous studies use suboptimal vector dataset, and proxies for environmental drivers to map vector distribution or provide basis for vector surveillance. However, the uncertainties linked to the original dataset are not always well documented, in particular regarding proxies for environ‐ mental drivers derived from satellite imagery. Analysis methods also do not always take into account the specificity of species ecological distribution and inaccuracies linked to the vector

Spatial surveillance of vector-borne diseases should integrate specialised knowledge in entomology, ecology, parasitology, epidemiology, human health, ecological modelling and social sciences. The authors of this chapter are specialized in those different fields and teamed up to offer an overview of the challenges posed by spatial surveillance. As vector-bornediseases are linked to the environment, a spatial analysis using geographical information or

Reliable outputs to go from *Anopheles* to spatial surveillance first depend on the data entering any analysis or decision process, being the data on *Anopheles* or on environmental factors. Environmental factors provided by remote sensing techniques that could be used to predict distribution or occurrence of malaria and *Anopheles* have already been reviewed [10]. Based on this inventory, we analyse pros and cons of *Anopheles* sampling strategies, various types of data and modelling techniques. Finally, useful initiatives to make research efforts available and operational in the field are discussed. The general scheme is provided in figure 1.

effective operational surveillance is not straightforward.

448 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**2. Problem statement and application area**

**3. Research course and method used**

**4. Following the road map**

remote sensing related technologies seems then appropriate.

dataset.

Many useful attributes can be collected on *Anopheles* such as the list of species and their vector status in a given area, resistance to insecticides, behaviour influencing human vector contact, control effort avoidance (early biting, outdoor resting). Research and monitoring programmes might be based on existing entomological data whose particularities should be dealt with at the modelling step. However the most direct way is to design the collection protocol in relation to the objective, i.e. mapping the *Anopheles* species. In this last case, the quality of the dataset could be high if some rules are followed. Monitoring data are typically collected in a network of sampling locations according to a variety of standardized procedures [11] and used for mapping species distributions [12]. However, records are generally collected only in a restricted number of locations often loosely distributed across the region of interest, which is inconvenient for documenting species distribution.

Species distribution modelling techniques [13,14] provide assistance to achieve mapping based on monitoring data [15] such as detailed further in the road map. When coupling *Anopheles* monitoring and mapping efforts, defining an optimal sampling strategy becomes of highest interest. Indeed, well-designed monitoring projects have the potential to produce appropriate data to estimate changes in species attributes [16] but also document the distribution in space and time [12]. An appropriate sampling design should address key issues: what constitutes a sampling location? How many are needed? Where do they have to be located? How often to survey? When monitoring data are used to generate species distribution models, designing the sampling strategy is a challenge because these issues are to be addressed relatively to the monitoring and the modelling objectives.

#### *4.1.1. Optimizing sample size*

Sampling locations may be sites, squares, transects or any spatial unit from which the meas‐ urements are made in the field to document attributes (e.g. presence, population density, infected/infective mosquitoes, reproductive status, insecticide resistance) that describe the *Anopheles* species. A sample is a set of sampling locations where attributes of the species are measured to estimate its characteristics over the entire study area. Hence, a sample must be representative of the whole study area and more than one sampling location is needed to account for the variation in the measurements made in the field. For instance, the population density or even the presence of a species depend on environmental conditions and this is to be taken into account to estimate the mean population density or the infection rate of the species in the study area or to document its spatial distribution with sufficient accuracy. Precision (typically measured by standard error) reflects how similar to each other are the different measurements made in the sampling locations, thereby providing a measure of sampling uncertainty. When sample measurements are similar to each other, the sample mean is likely to be estimated with an acceptable level of precision from a few sampling locations. In contrast, when the between-location variation in the measurements is high, a larger number of sampling locations is needed [11]. Achieving a sufficient level of precision is of critical importance: the higher the precision of the estimates, the better the chances to detect temporal changes using statistical hypothesis testing procedures. Sample size is also known to impact on the perform‐ ance of species distribution models [17–19]: predictions based on few records are likely to be less accurate than predictions based on larger sample sizes [18]. A sufficient number of sampling locations is needed to capture in the statistical models the response of the species to the environmental conditions. A balance is, therefore, to be achieved between ensuring statistical robustness (i.e. increasing the sample size) and reducing sampling effort (i.e. decreasing the sample size) because sampling is time- and/or budget-consuming.

For monitoring purpose, a power analysis may be performed to evaluate the number of sampling locations required to detect a given level of change over time in the attributes of the species with a predetermined level of statistical certainty. First, decisions are to be made by the users on (1) the minimum level of change that is to be detected in the analysis (for instance, 10% of change between time t and t+1) and (2) the acceptable chances of making type-1 (i.e. concluding that change is taking place when it is not) and type-2 (i.e. concluding that no change is taking place when it is) errors in hypothesis testing procedures [15]. Such decisions are often based on the precautionary principle and the relative importance of type-1 and type-2 errors also depends on the objective. Then, the analysis integrates information on the precision of the estimates to calculate the optimal sample size needed to detect the desired level of change. A pilot survey is, however, required to obtain an initial approximation of the precision of the estimates linked to the variation in the field measurements. For modelling applications, modelling performance increases with sample size and impact of sample size on modelling performance may strongly depend on the modelling technique used [20]. A series of studies have also recently shown that the performance may be sensitive to particularly small sample sizes and may reach an asymptote level beyond a sufficiently large sample size [18,19]. In order to examine how large the sample size should be to obtain sufficiently well-performing models, different alternative options are available: (1) using readily available datasets in the study area [12] or (2) creating virtual species in real landscapes [19,21]. With such data, it becomes possible to manipulate the number of sampling locations to represent a range of sample size and to examine the impact of restricted sample size on modelling performance.

### *4.1.2. Optimizing sampling strategy in space*

the sampling strategy is a challenge because these issues are to be addressed relatively to the

Sampling locations may be sites, squares, transects or any spatial unit from which the meas‐ urements are made in the field to document attributes (e.g. presence, population density, infected/infective mosquitoes, reproductive status, insecticide resistance) that describe the *Anopheles* species. A sample is a set of sampling locations where attributes of the species are measured to estimate its characteristics over the entire study area. Hence, a sample must be representative of the whole study area and more than one sampling location is needed to account for the variation in the measurements made in the field. For instance, the population density or even the presence of a species depend on environmental conditions and this is to be taken into account to estimate the mean population density or the infection rate of the species in the study area or to document its spatial distribution with sufficient accuracy. Precision (typically measured by standard error) reflects how similar to each other are the different measurements made in the sampling locations, thereby providing a measure of sampling uncertainty. When sample measurements are similar to each other, the sample mean is likely to be estimated with an acceptable level of precision from a few sampling locations. In contrast, when the between-location variation in the measurements is high, a larger number of sampling locations is needed [11]. Achieving a sufficient level of precision is of critical importance: the higher the precision of the estimates, the better the chances to detect temporal changes using statistical hypothesis testing procedures. Sample size is also known to impact on the perform‐ ance of species distribution models [17–19]: predictions based on few records are likely to be less accurate than predictions based on larger sample sizes [18]. A sufficient number of sampling locations is needed to capture in the statistical models the response of the species to the environmental conditions. A balance is, therefore, to be achieved between ensuring statistical robustness (i.e. increasing the sample size) and reducing sampling effort (i.e.

decreasing the sample size) because sampling is time- and/or budget-consuming.

For monitoring purpose, a power analysis may be performed to evaluate the number of sampling locations required to detect a given level of change over time in the attributes of the species with a predetermined level of statistical certainty. First, decisions are to be made by the users on (1) the minimum level of change that is to be detected in the analysis (for instance, 10% of change between time t and t+1) and (2) the acceptable chances of making type-1 (i.e. concluding that change is taking place when it is not) and type-2 (i.e. concluding that no change is taking place when it is) errors in hypothesis testing procedures [15]. Such decisions are often based on the precautionary principle and the relative importance of type-1 and type-2 errors also depends on the objective. Then, the analysis integrates information on the precision of the estimates to calculate the optimal sample size needed to detect the desired level of change. A pilot survey is, however, required to obtain an initial approximation of the precision of the estimates linked to the variation in the field measurements. For modelling applications, modelling performance increases with sample size and impact of sample size on modelling performance may strongly depend on the modelling technique used [20]. A series of studies

monitoring and the modelling objectives.

450 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

*4.1.1. Optimizing sample size*

An appropriate sampling design also involves positioning the sampling locations so that the full range of environmental conditions across the study area may be covered to ensure the representativeness of the sample. Several approaches are available to position the sampling locations (only some are presented below) [21] with different advantages and disadvantages (details in [11]): Those include:

**Expert-based sampling** – Sampling units are located based on a priori knowledge of the study area and the status of the species. This subjective strategy is to be avoided because the sample is most likely not representative of the study area and may thus not be used for statistical inference.