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Effects of landscape composition on mosquito population in urban green spaces
Urban Forestry & Urban Greening 49 (2020) 126626
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Urban Forestry & Urban Greening journal homepage: www.elsevier.com/locate/ufug
Original article
Effects of landscape composition on mosquito population in urban green spaces
T
Jingwei Zhao*, Ting Tang, Xinxin Wang School of Architecture and Design, China University of Mining and Technology, Xuzhou, Zip Code: 221116 China
A R T I C LE I N FO
A B S T R A C T
Editor: T Timothy Van Renterghem
Despite the fact that urban green spaces provide multiple benefits to urban residents, they have some negative effects on people, such as mosquito bites which not only cause major nuisance and debase visitors’ experiences, but also transmit infectious diseases, discouraging people access to urban green spaces. In order to find the effects of landscape composition on mosquito population and provide practical methods of design to control mosquitoes, ten sample sites of urban green spaces in Xuzhou, eastern China were selected and their mosquito densities were measured using the light traps during the evening in summer 2019. And, the satellite image with a radius of 300 m centered by the light trap on each site was used to quantify the landscape composition of the ten sites studied. The results indicate that (1) more aquatic plants can predicate a higher mosquito density, and more water implies an environment with more mosquitoes; (2) in general, the higher the wood plant coverage is, the fewer mosquitoes are. These results provide valuable guidance and reference for anti-mosquito landscape design and management.
Keywords: Mosquito density Landscape composition Mosquito control Landscape design
1. Introduction Urban green spaces have multiple benefits for sustainability, such as improving air quality (Matos et al., 2019), maintaining the balance between carbon and oxygen (Chen, 2015), regulating temperature and humidity (Oliveira et al., 2018; Feyisa et al., 2018) and enriching biodiversity (Aronson et al., 2017). Also, they provide human with outdoor spaces for physical exercise, entertainment, relaxation and stress relief (Zlot and Schmid, 2005; Nordh et al., 2009; Karjalainen et al., 2010; Branas et al., 2011), which plays an increasing role for human life. However, it is not only human being enjoying urban green spaces, the wildlife such as mosquito shares them with us (Byrne and Yang, 2009; Wong and Jim, 2017). Mosquito species is a negative component of biodiversity in urban green spaces (Schorn et al., 2011). They have a strong colonial ability to live successfully in human-dominated environments (Byrne and Yang, 2009；Gómez-Baggethun et al., 2013). The physiological habits of mosquito suggest that most individuals generally choose to carry out the host-seeking activities at twilight and nighttime (Lu, 1999). They are also the time when people conduct outdoor activities in urban green spaces due to the busy daytime. The nuisance caused by mosquitoes has a negative impact on visitors, which can prevent people from outdoor activities, especially in hot-rainy summer (Medeiros-Sousa et al., 2015) which is supportive for mosquito
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activities. More seriously, mosquito bites can transmit infectious diseases such as dengue fever, malaria and yellow fever (Lee et al., 2018). Many researchers have realized the significance of urban green spaces as unintentional habitats for mosquitoes which can cause more negative effects on human due to the dense population in urban areas, (Maimusa et al., 2016; Heinisch et al., 2019). Unfortunately, the epidemic-prevention function of urban green spaces is not systematically studied, and our understanding on mosquito control using landscape design in urban green spaces is limited. Existing literature have launched investigations into the mosquitoes in urban green spaces (Medeiros-Sousa et al., 2015, 2017; Wong and Jim, 2016, 2017, 2018; Harbison et al., 2017). Wong and Jim (2016) believed that landscape types could provide reliable explanatory power of mosquito population, and the authors also called for a better understanding of ecological habits of mosquitoes in different landscapes to improve the effectiveness of mosquito control. A study in Brazil revealed that a considerable number of mosquitoes was collected in water such as ponds and puddles (Medeiros-Sousa et al., 2015), and an investigation in Chicago claimed that more immature mosquitoes could be found in the basins with three or more deciduous trees within 20 m (Harbison et al., 2017). While Heinisch et al. (2019) found that the highest density of mosquito was associated with the interior area in a park with higher vegetation coverage, and emphasized the importance
Corresponding author at: School of Architecture and Design, China University of Mining and Technology, Xuzhou, China. E-mail addresses: [email protected] (J. Zhao), [email protected] (T. Tang), [email protected] (X. Wang).
https://doi.org/10.1016/j.ufug.2020.126626 Received 27 October 2019; Received in revised form 7 January 2020; Accepted 31 January 2020 Available online 01 February 2020 1618-8667/ © 2020 Elsevier GmbH. All rights reserved.
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the whole recordset. These species are bloodthirsty (Xia et al., 2018). A total of ten sample sites were selected using stratified random sampling method and the center point of any two sites should be at least 300 m apart. They located at three types of green spaces. The campus of China University of Mining and Technology (Site A, Site B and Site C) represented the typical green spaces in urban areas. They possessed relatively more man-made elements and higher degree of vegetation maintenance. Quanshan Forest Park (Site D, Site E and Site F) was dominated by natural woods. Their overall vegetation height was higher than other sites, while the degree of vegetation maintenance was relatively low. And Yunlong Lake Scenic Spot (Site G, Site H, Site I and Site J) was a famous local scenic spot due to the beautiful waterscape. Its vegetation was composed of trees, shrubs and grass, which created many semi-open spaces. The generally voluntary dispersal distance of the majority of mosquitoes (over 90%), based on mark-recapture experiments, was less than 1000 m (without considering the effect of wind), and of which 90% mosquito species were within 100 m (Byran et al., 1991). It has been suggested that the general flight distance of over 80% mosquito species could be treated as a standard distance (100 m) to study the mosquito activities (Knight et al., 2003; Verdonschot and BesseLototskaya, 2014). Due to not identifying mosquito species in this study (see 3.3. Mosquito density measurement), the authors wanted to calculate landscape composition in a larger area to establish a reliable relationship between landscape variables and mosquito density, because the collected mosquitoes in this study might include some species which could voluntary dispersal more than 100 m. Thus each site was designed as a circle with a radius of 300 m centered on the mosquitotrapping point, which led to two pairs of sites with partially overlapping coverage (see Fig. 1). Despite partial overlaps of two pairs of sites, their landscape compositions still had significant heterogeneity. Each site covered an area of 28.26 ha, and overall areas of the ten sample sites were about 262.09 ha.
of the vector ecology and microtopographic distribution of mosquitoes. These findings seem to suggest that landscape patterns impact on mosquito population. The previous works, however, mainly described the mosquito habitats qualitatively according to field identification (Brown et al., 2008a), a more systematically and definitely quantitative landscape analysis is needed to properly assess the impact of the landscape composition on mosquito population, and provide evidence to guide anti-mosquito landscape design. The satellite remote sensing can provides a cost-effective method to quantify the landscape composition of mosquito’s habitats (e.g. Impoinvil et al., 2008; Severini et al., 2008; Chang et al., 2009). At macro-scale, many literatures indicated that the distribution of mosquitoes was affected by land use types (Junglen et al., 2009), urbanization process (Ferreira and Alecrim, 2004), spatial distribution of water area (Hassan and Onsi, 2004) and geographical landscape (Medeiros-Sousa et al., 2017). Most mosquitoes lived in the residential areas with high human density (Junglen et al., 2009), especially the villages and the slums with poor environmental quality (Hassan et al., 2013). In urban environments, previous studies suggested that the larger the green area and the higher the degree of patch separation, the more the mosquito abundance; also, the most heterogeneous landscape hid the richest number of mosquitoes (Brown et al., 2008a; Chaves et al., 2011; Medeiros-Sousa et al., 2017). The accurate target vector control requires the fine-scaled study in urban environments (Little et al., 2017a). At micro-scale, however, there is limited research on the distribution of mosquitoes in urban green spaces (Landau and Van Leeuwen, 2012). Although, a few studies found that plant diversity, impervious surfaces, the number of structures, medium height trees, soil, open spaces and vacant lots were closely related with mosquito density (Landau and Van Leeuwen, 2012; Little et al., 2017b; Yang, et al., 2019), none of these studies proposed the availably systematic methods for mosquitoes control. Furthermore, a small amount of investigations can not provide reliable guidance for the anti-mosquito design strategy in urban green spaces. Lõhmus and Balbus (2015) claimed that we should pay a great effort to prevent mosquito breeding in the phrase of design, rather than after the occurrence of mosquitoes in urban green spaces.
3.2. Landscape composition measurement Google Earth has been employed in public health monitoring in recent years (Boulos et al., 2011; Liang et al., 2018). A global satellite image at spatial resolutions from Landsat (15–30 m) to QuickBird (60 cm) carries by Google Earth have been released freely to the public, and could be used without any technical barriers (Liang et al., 2018). The present study used Google Earth at the level 20 to acquire remotesensing images of the ten sites for landscape composition measurement (Chang et al., 2009; Xie et al., 2019). The ten images were photographed on 23 June 2018, which were the nearest images we could find to the time when the experiments were conducted (from July to August 2019). Therefore, field observation by the authors was performed to validate the images, and the authors did not find identifiable changes between the field observation and the satellite images on the ten sites. Finally, landscape composition were classified into four types: (1) vegetation which included three subtypes (grass, woods, and aquatic plants); (2) water which included two subtypes (lake and stream); (3) man-made elements which included three subtypes (buildings, squares and roads); and (4) others which included one subtype (bare land) (see Table 1). In order to quantify landscape composition, a grid of 2826 squares (10 m × 10 m) was covered on the image of each site using the Photoshop software. Any subtype that covered more than half of a square was marked and computed (Nordh et al., 2009; Zhao et al., 2013a). At last, the proportion of the landscape occupied by each habitat type was figured out.
2. Aims and overall framework of the study In order to encourage more people contact with green spaces, reduce mosquito nuisance and provide a specific guideline for anti-mosquito landscape design, the present study used Google Earth to obtain data of landscape composition of ten sample sites of urban green spaces in Xuzhou, eastern China. Meanwhile, the mosquito density on the ten sites was measured using mosquito traps. The following questions guided this study. (1) What is the relationship between mosquito density and landscape composition in urban green spaces? (2) How to manipulate the landscape composition to control mosquito in landscape design and management? 3. Methods 3.1. Study areas and sample sites This study was carried out in Xuzhou, Jiangsu Province, eastern China (16°22′−118°40′ E; 33°43′−34°58′ N). Xuzhou is a mediumsized city. Its climate is a typical temperate monsoon with a hot and humid summer (from May to September), and mosquito population peaks in July and August (Li et al., 2016a), because the season is suitable for mosquito survival and reproduction. According to local mosquito surveillance reports (Li et al., 2016a; XZCDC, 2018), the common mosquito species in this area include Culex pipiens pallens, Culex tritaeniorhynchus, Anopheles sinensis, and Aedes albopictus, among which Culex pipiens pallens is the dominant species, accounting for more than 60% of
3.3. Mosquito density measurement In order to remove the occasional factors as far as possible, such as wind, temperature, humidity on each site, the collection of mosquitoes 2
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Fig. 1. Ten sample sites distributed in the study area.
and turned on at the beginning of the evening twilight (19:30), after 2.5 h, the mosquitoes (including dead and alive) were collected and numbered.
Table 1 Landscape composition classes. Type
Vegetation
Water
Man-made elements
Others
Subtype
Grass Woods Aquatic plants
Lake Stream
Buildings Squares Roads
Bare lands
3.4. Statistical analysis SPSS 17 software was employed to analyze the data. After the normality of the data were verified (only the data of Site F had not a normal distribution (Sig. = 0.001), others had normal distributions (Sig. > 0.05)), the One-way ANOVA was used to determine the differences of mosquito density among the ten sites, and the homoscedasticity of the data was also tested. Furthermore, LSD was used for pairwise comparison. Then, the correlations analysis was conducted to explore the relationships between mosquito density and landscape composition, and the stepwise multiple linear regression was used to find the significant drivers of landscape composition for mosquito density.
was performed six times, from July 15 to August 24, 2019 with a fiveday interval, and the actual dates were shown in Table 2. Two collection dates of July 26, August 19 were postponed due to rain or strong wind on the scheduled capturing days (see Table 2). In this study, adult mosquitoes were captured by CDC (Centers for Disease Control) light traps (Silver, 2007). The effectiveness of the equipment has been evidenced by previous works (Wheeler et al., 1996; Li et al., 2016b; Hast et al., 2019). All traps were almost the same height (1.2 m above the ground). The location of trap on each site should be relatively open and dark to avoid the trap lighting was blocked by objects such as tree branches and interfered by existing lighting. These traps were emptied
Table 2 Number of mosquitoes captured by six times on ten sites.
First capture Second capture Third capture Fourth capture Fifth capture Sixth capture Total
July 15 July 26 July 31 Aug 05 Aug 19 Aug 24
Site A
Site B
Site C
Site D
Site E
Site F
Site G
Site H
Site I
Site J
14 3 5 10 2 22 56
3 4 8 10 4 15 44
15 6 14 19 14 38 106
8 1 2 6 3 2 22
5 1 0 3 1 0 10
33 9 8 9 5 5 69
52 27 62 52 54 130 377
42 23 36 30 12 28 171
7 5 10 7 6 0 35
28 28 46 48 23 59 232
3
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Table 3 Percentage of area covered by landscape composition variables on ten sites.
Site Site Site Site Site Site Site Site Site Site
A B C D E F G H I J
Grass (%)
Woods (%)
Aquatic plants (%)
Total vegetation (%)
Lake (%)
Stream (%)
Total water (%)
Buildings (%)
Squares (%)
Roads (%)
Total man-made elements (%)
Bare land (%)
19.43 30.6 15 13.2 5.58 16.49 9.09 3.22 14.23 7.4
34.82 27.92 20.59 74.24 90.27 63.13 30.47 15.75 37.47 9.02
1.34 1.2 0.05 1.2 0 2.09 7.43 5.8 3.4 0.96
55.59 59.72 35.64 88.64 95.85 81.71 46.99 24.77 55.1 17.38
4.71 9.02 0.81 4.25 0 4.78 37.79 60.62 31.92 76.22
2.87 0 2.12 0 0 0 0 1.52 0 0
7.58 9.02 2.93 4.25 0 4.78 37.79 62.14 31.92 76.22
11.04 7.23 12.81 0.42 1.85 0.39 2.69 5.38 0.42 0.28
1.88 6.97 11.89 0.57 0.28 3.11 2.94 1.27 5.84 3.68
10.53 10.58 12.17 6.12 1.52 10.01 10.79 6.44 6.72 2.44
23.45 24.78 36.87 7.11 3.65 13.51 16.42 13.09 12.98 6.4
13.38 6.48 24.56 0 0.5 0 2.8 0 0 0
4. Results
analysis only checked the relationship between mosquito density and landscape composition variables individually. Previous works claimed that the regression analysis could solve this problem (Zhao et al., 2013b). The stepwise multiple linear regression analysis, mosquito density as dependent and landscape composition variables as independents, showed that the amount of aquatic plants is the reliable predictor of mosquito density (Table 4). The validity of the model shown in Table 5 was tested. The normality of the residuals was demonstrated by using the KolmogorovSmimo test (Kolmogorov-Smirnov Z = 0.639; p = 0.809). Variance analysis result revealed a linear correlation between the landscape composition variables and the mosquito density (F = 7.583, p = 0.026). Because of this model only included one predicator, there was no problem with multi-collinearity. Thus, this model was accepted.
4.1. Overall number of adult mosquitoes The number of mosquitoes captured by six times on ten sample sites was shown in Table 2. A total of 1122 adult mosquitoes were collected, in which the highest mosquito population was captured on Site G, and the lowest on Site E. The one-way ANOVA showed that there was a significant difference of mosquito population among the ten sites F = 12.099; p < 0.001; Levene’ Sig. = 0.08, and 24 of 45 pairwise comparisons were significantly different. These results implied that environmental features had an essential effect on mosquito density. 4.2. Relationships between landscape composition and mosquito density
5. Discussion
The measured proportion of landscape composition for each site was presented in Table 3. The mean mosquito numbers of six captures were used as mosquito density on each site. The correlation analysis indicated that mosquito density increased with the increase of aquatic plants, lake and total water coverage (Table 4). Despite of the fact that there were significantly linear relations among landscape composition variables (see Table 4), the correlation
5.1. Aquatic plants in relation to mosquito density The study indicates that the amount of aquatic plants is the reliable predicator of mosquito density. This result replicates the finding of existing works (Walton et al., 2012; Yadav et al., 2012; Lõhmus and
Table 4 Correlations between mosquito density and landscape composition variables (Pearson).
Mosquito density Grass Woods Aquatic plants Total vegetation Lake Stream Total water Buildings Squares Roads Total man-made elements
Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed)
Grass
Woods
Aquatic plants
Total vegetation
Lake
Stream
Total water
Buildings
Squares
Roads
Total manmade elements
Bare lands
－0.405 0.246
－0.552 0.098 －0.067 0.855
0.694* 0.026 －0.337 0.341 －0.321 0.366
－0.614 0.059 0.201 0.579 0.961** 0.000 －0.332 0.349
0.652* 0.041 －0.520 0.123 －0.644* 0.045 0.500 0.141 －0.760* 0.011
－0.074 0.839 0.065 0.859 －0.362 0.305 －0.103 0.778 －0.356 0.313 －0.157 0.665
0.652* 0.041 －0.520 0.123 －0.662* 0.037 0.499 0.142 －0.778** 0.008 0.999** 0.000 －0.117 0.747
－0.083 0.820 0.387 0.269 －0.406 0.244 －0.205 0.570 －0.314 0.377 －0.332 0.349 0.851** 0.002 －0.300 0.400
－0.007 0.985 0.449 0.193 －0.492 0.148 －0.239 0.507 －0.385 0.271 －0.149 0.682 0.214 0.552 －0.141 0.698 0.535 0.111
0.124 0.732 0.609 0.062 －0.317 0.372 0.206 0.568 －0.119 0.743 －0.387 0.269 0.426 0.219 －0.372 0.290 0.621 0.056 0.545 0.103
0.005 0.990 0.561 0.092 －0.478 0.162 －0.104 0.774 －0.325 0.359 －0.348 0.324 0.627 0.052 －0.325 0.360 0.880** 0.001 0.800** 0.005 0.846** 0.002
－0.076 0.834 0.367 0.297 －0.326 0.357 －0.351 0.319 －0.253 0.481 －0.420 0.227 0.735* 0.015 －0.393 0.261 0.910** 0.000 0.714* 0.020 0.627 0.052 0.904** 0.000
*Significance at the 0.05 level; **Significance at the 0.01 level. 4
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Table 5 Significant predictor for the mosquito density in urban green spaces emerging from stepwise multiple linear regression analysis. Dependent
Mosquito density (R² = 0.482; adjusted R² = 0.418)
Independent
(constant) Aquatic plants
Unstandardized Beta
35.576 32.648
Standardized Beta
0.694
T
0.895 2.730
Significance
0.397 0.026
Collinearity statistics Tolerance
VIF
1.000
1.000
suitable for mosquitoes to live in. At last, mosquitoes have a behavioral bias named appetitive flight for host seeking (Service, 1997), and the female mosquitoes are bloodthirsty, thus mosquito population is generally related to the amount of host (Verdonschot and Besse-Lototskaya, 2014; Heinisch et al., 2019). In urban green spaces, humans are more likely to be the potential hosts because only a few animals live in urban green spaces (Halasa et al., 2014; Lounibos and Kramer, 2016). But vegetation, especially the woods, is not conducive to human access, especially during the evening. That is to say mosquito density is lower in woods due to less potential hosts.
Balbus, 2015). Aquatic plants have some favorable features for mosquito life. Firstly, aquatic plants signify a water area, which indicate not only enough plant juices but also water resource, implying that the local mosquitoes do not need risk-laden active dispersal from energyintensive areas to further places (Wong and Jim, 2018). Secondly, the dense stems of aquatic plants tend to intercept flowing water and create stagnant water (Greenway, 2003) which has been evidenced a major factor related to more mosquito larvae (Russell, 1999; Yadav et al., 2012), because a relatively static water area can prevent mosquito eggs from being washed away (Batzer and Resh, 1992). Thirdly, the predators as biological agents in controlling mosquito population have been widely reported (Knight et al., 2003; Kumar and Hwang, 2006; Dale et al., 2007). Aquatic plants colony is not conducive to the predators entering the interior of the plant community to hunt due to the combination of dense aquatic plants and the accumulation of decaying plant materials (Walton, 2007; Walton et al., 2012). And, low dissolved oxygen accompanied by intensive aquatic plants in water is another factor of inhabitation of predators survival (Thullen et al., 2002), but does not affect the mosquitoes (Nelson and Thullen, 2008; Lõhmus and Balbus, 2015).
5.4. Application for mosquito control in urban green spaces In order to cut down mosquito population in urban green spaces, in turn mitigating the nuisance and reducing vector-borne infectious diseases, this study give a clear clue to guide landscape design: reducing aquatic plants and water area, and setting more woodland in urban green spaces. In practice, it needs to pay more attention to the drainage design of rainfall to avoid the presence of temporary water area in rainy season. Maybe, the most economical way is to increase the permeability of the ground, this method also contribute to urban sustainable development (Zhou et al., 2018). Concerning the fact that aquatic plants are the solely predicator of mosquito density, we should cut down the percentage of water covered by aquatic plants in urban green spaces. However, designers have to consider the fact that the main aquatic plants in the ten sites studied are Nelumbo nucifera, Nymphaea tetragona, Lemna minor, Phragmites communis, Iris pseudacorus, and Typha orientalis. It is unclear whether other species contribute to mosquito population. Meanwhile, woody plants are encouraged in urban green spaces. They not only have more ecological values than grass, but also can decrease the mosquito population. At last, finding mosquito’s predators is never the aim of this study, but biological enemies such as water boatmen, backswimmers and mosquito fish can be used to control mosquito population (Knight et al., 2003; Kumar and Hwang, 2006; Dale et al., 2007). For this issue, we have to create suitable habitats to support the predators’ survival and reproduction. When applying these results to practice, we must keep in our minds that urban green spaces bear multiple functions such as aesthetic, recreation and ecological sustainability. It should be noted that mosquitoes control as one of the design goals is not always consistent with other appeals. For example, according to the results of this study, waterscape will increase mosquito density, however, the waterscape in urban areas claims a high rating of visual aesthetic quality (Herzog and Barnes, 1999; Kaltenborn and Bjerke, 2002; Lee, 2017;) and positive effects on mental stress relief of visitors (Zhao et al., 2018). Similarly, despite the fact that aquatic plants were positively associated with mosquito density (Table 5), they have an important contribution to ecological service such as water purification (Chambers et al., 2008; O’Hare et al., 2017; Lu et al., 2018). Medeiros-Sousa et al. (2015) suggest that the complete removal of mosquito habitats possibly causes the risks of ecological degradation. Thus, landscape design has to reach a balance among the multiple functions provided by a landscape, and maximize the comprehensive benefits. However, mosquito control is generally ignored by landscape architects. This study suggests that we should take mosquito control into account in the design process of urban green spaces.
5.2. Water in relation to mosquito density It is well-known that water is necessary for mosquito breeding. In urban green spaces, most mosquito species live in permanent water body such as vegetated lakes, ponds, creeks and rivers (Crocker et al., 2017), or temporary water body such as puddles formed after rainfall or irrigation (Russell, 1999; Medeiros-Sousa et al., 2015). Therefore, the presence of water can support mosquito survival and reproduction. This result is also demonstrated by the present study which suggests that there is a statistically positive correlation between water area and mosquito density (see Table 4). Biologically, mosquito is completely metamorphosed insect. Its life consists of four stages: egg, larva, pupa and adult, of which the first three stages need grow in water. Although adult male mosquitoes migrate to plant communities on land, female adult mosquitoes still live in the appropriate water or moist settings as the spawning base after mating. Thus, the larger water area in a landscape is, the more mosquitoes live in. Obviously, it also shows us a clear clue that the key to reduce mosquito density is “water control”. 5.3. Land vegetation in relation to mosquito density Despite some benefits that plant community provides to mosquito, such as sugar sources and shelters (Foster, 1995；Brown et al., 2008b; Hayden et al., 2010), this study concludes that land vegetation is not frequented by mosquitoes in urban green spaces (Table 4). This result possibly is explained by the following three reasons. Firstly, the crown of trees can retain a part of rainfall, and the natural soil under the trees penetrates rain water more quickly than pavements (Dunne et al., 1991; Sarr et al., 2001; Harden and Scrugges, 2003), which cause less surface water in forest and less static water such as puddles to support mosquito breeding. Secondly, Junglen et al. (2009) concludes that plant height has a major impact on mosquito population, the taller the plants, the fewer mosquitoes. The finding of this study generally supports it (see Table 4, the mosquito density has a closely significantly negative relationship with woods). It can be speculated that tall vegetation is not 5
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5.5. Limitations and future research
influence the work reported in this paper.
Firstly, the trapped mosquitoes were not classified by species and sex in our study. In fact, different habitats can support different mosquito species (Gleiser and Zalazar, 2009; Landau and Van Leeuwen, 2012) which maybe differ in epidemiological significance (Xia et al., 2018), and only female mosquitoes need blood meals, which are the culprits of the nuisance and risk of infection (Diagana et al., 2007). Therefore, in a future study, researchers need to find the species composition of mosquito on each sample site, the most popular species should be paid more attention. Meanwhile, the female mosquitoes should be identified and their preferred habitats should be regarded as the priority intervention project in the design and management of urban green spaces. Secondly, a full test of population density requires the detection of larvae (Medeiros-Sousa et al., 2015, 2017; Maimusa et al., 2016; Heym et al., 2018; Heinisch et al., 2019), our experiments, however, only collect the adult mosquitoes. Although immature mosquitoes can not cause nuisance and spread diseases, their density can predict the amount of adult mosquitoes. If the design of anti-mosquito landscape can focus on controlling the larvae, maybe, it is more efficient than focusing on the adults (Wong and Jim, 2018). Thirdly, many scholars have suggested that meteorological factors such as temperature, humidity and wind also play a role in mosquito aggregation (Daszak et al., 2000; Reiter, 2001; Reiter and Lapointe, 2007; Codeço et al., 2015). And different sites in urban green spaces can form different microclimates (Wong and Jim, 2017; Yang et al., 2019a,2019b), which possibly affect the mosquito density on the sites. However, this present study does not take the microclimate into account. There are few reports to answer the question of how to use landscape design to influence microclimate and further affect the mosquito density, it is necessary to conduct researches to explore this issue in the future, which maybe provide practical guidance to antimosquito landscape design. At last, this study concluded that aquatic plants and water have a significant relationship with mosquito density. However, aquatic plants and water possess some specific features, such as plant morphology, species composition, configuration of aquatic plants on the water, and the size, quality, shape and depth of water. These features possibly contribute differently to mosquito control. Further research is needed to investigate the extent to which aquatic plants and water function as mosquito habitats in urban green spaces, which will deepen our finescaled understanding on the relationships between environmental features and mosquito population and provide practical guidance to landscape design.
Acknowledgement This research is supported by the Fundamental Research for the Central Universities (2017WB09). References Aronson, M.F.J., Lepczyk, C.A., Evans, K.L., Goddard, M.A., Lerman, S.B., MacIvor, J.S., Nilon, C.H., Vargo, T., 2017. Biodiversity in the city: key challenges for urban green space management. Front. Ecol. Environ. 15, 189–196. Batzer, D.P., Resh, V.H., 1992. Wetland management strategies that enhance waterfowl habitats can also control mosquitoes. J. Am. Mosq. Control Assoc. 8, 117–125. Boulos, M.N.K., Resch, B., Crowley, D.N., Breslin, J.G., Chuang, K.Y.S., 2011. Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. Int. J. Health Geogr. 10, 67. Branas, C.C., Cheney, R.A., MacDonald, J.M., Tam, V.W., Jackson, T.D., Ten Have, T.R., 2011. A difference-in-differences analysis of health, safety, and greening vacant urban space. Am. J. 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6. Conclusion This study explores the effects of landscape composition on mosquito density in urban green spaces. It expects that landscape architects consider mosquito ecology in the design stage of urban green spaces, and use design strategy to cut down mosquito density. The results claim that more aquatic plants and higher water coverage in a landscape indicate higher mosquito density. Although there is a non-significant relationship between woody plants and mosquito density, generally speaking, woody plants are not conductive to mosquito development. Despite of some limitations, these results inform landscape architects that they can control mosquitoes in urban green spaces by manipulating the landscape composition variables which have a significant impact on mosquito population. For example, reducing the amount of water, controlling aquatic plants, and planting more woods in a landscape are efficient ways to decrease mosquito population. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to 6
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Urban Forestry & Urban Greening journal homepage: www.elsevier.com/locate/ufug
Original article
Effects of landscape composition on mosquito population in urban green spaces
T
Jingwei Zhao*, Ting Tang, Xinxin Wang School of Architecture and Design, China University of Mining and Technology, Xuzhou, Zip Code: 221116 China
A R T I C LE I N FO
A B S T R A C T
Editor: T Timothy Van Renterghem
Despite the fact that urban green spaces provide multiple benefits to urban residents, they have some negative effects on people, such as mosquito bites which not only cause major nuisance and debase visitors’ experiences, but also transmit infectious diseases, discouraging people access to urban green spaces. In order to find the effects of landscape composition on mosquito population and provide practical methods of design to control mosquitoes, ten sample sites of urban green spaces in Xuzhou, eastern China were selected and their mosquito densities were measured using the light traps during the evening in summer 2019. And, the satellite image with a radius of 300 m centered by the light trap on each site was used to quantify the landscape composition of the ten sites studied. The results indicate that (1) more aquatic plants can predicate a higher mosquito density, and more water implies an environment with more mosquitoes; (2) in general, the higher the wood plant coverage is, the fewer mosquitoes are. These results provide valuable guidance and reference for anti-mosquito landscape design and management.
Keywords: Mosquito density Landscape composition Mosquito control Landscape design
1. Introduction Urban green spaces have multiple benefits for sustainability, such as improving air quality (Matos et al., 2019), maintaining the balance between carbon and oxygen (Chen, 2015), regulating temperature and humidity (Oliveira et al., 2018; Feyisa et al., 2018) and enriching biodiversity (Aronson et al., 2017). Also, they provide human with outdoor spaces for physical exercise, entertainment, relaxation and stress relief (Zlot and Schmid, 2005; Nordh et al., 2009; Karjalainen et al., 2010; Branas et al., 2011), which plays an increasing role for human life. However, it is not only human being enjoying urban green spaces, the wildlife such as mosquito shares them with us (Byrne and Yang, 2009; Wong and Jim, 2017). Mosquito species is a negative component of biodiversity in urban green spaces (Schorn et al., 2011). They have a strong colonial ability to live successfully in human-dominated environments (Byrne and Yang, 2009；Gómez-Baggethun et al., 2013). The physiological habits of mosquito suggest that most individuals generally choose to carry out the host-seeking activities at twilight and nighttime (Lu, 1999). They are also the time when people conduct outdoor activities in urban green spaces due to the busy daytime. The nuisance caused by mosquitoes has a negative impact on visitors, which can prevent people from outdoor activities, especially in hot-rainy summer (Medeiros-Sousa et al., 2015) which is supportive for mosquito
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activities. More seriously, mosquito bites can transmit infectious diseases such as dengue fever, malaria and yellow fever (Lee et al., 2018). Many researchers have realized the significance of urban green spaces as unintentional habitats for mosquitoes which can cause more negative effects on human due to the dense population in urban areas, (Maimusa et al., 2016; Heinisch et al., 2019). Unfortunately, the epidemic-prevention function of urban green spaces is not systematically studied, and our understanding on mosquito control using landscape design in urban green spaces is limited. Existing literature have launched investigations into the mosquitoes in urban green spaces (Medeiros-Sousa et al., 2015, 2017; Wong and Jim, 2016, 2017, 2018; Harbison et al., 2017). Wong and Jim (2016) believed that landscape types could provide reliable explanatory power of mosquito population, and the authors also called for a better understanding of ecological habits of mosquitoes in different landscapes to improve the effectiveness of mosquito control. A study in Brazil revealed that a considerable number of mosquitoes was collected in water such as ponds and puddles (Medeiros-Sousa et al., 2015), and an investigation in Chicago claimed that more immature mosquitoes could be found in the basins with three or more deciduous trees within 20 m (Harbison et al., 2017). While Heinisch et al. (2019) found that the highest density of mosquito was associated with the interior area in a park with higher vegetation coverage, and emphasized the importance
Corresponding author at: School of Architecture and Design, China University of Mining and Technology, Xuzhou, China. E-mail addresses: [email protected] (J. Zhao), [email protected] (T. Tang), [email protected] (X. Wang).
https://doi.org/10.1016/j.ufug.2020.126626 Received 27 October 2019; Received in revised form 7 January 2020; Accepted 31 January 2020 Available online 01 February 2020 1618-8667/ © 2020 Elsevier GmbH. All rights reserved.
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the whole recordset. These species are bloodthirsty (Xia et al., 2018). A total of ten sample sites were selected using stratified random sampling method and the center point of any two sites should be at least 300 m apart. They located at three types of green spaces. The campus of China University of Mining and Technology (Site A, Site B and Site C) represented the typical green spaces in urban areas. They possessed relatively more man-made elements and higher degree of vegetation maintenance. Quanshan Forest Park (Site D, Site E and Site F) was dominated by natural woods. Their overall vegetation height was higher than other sites, while the degree of vegetation maintenance was relatively low. And Yunlong Lake Scenic Spot (Site G, Site H, Site I and Site J) was a famous local scenic spot due to the beautiful waterscape. Its vegetation was composed of trees, shrubs and grass, which created many semi-open spaces. The generally voluntary dispersal distance of the majority of mosquitoes (over 90%), based on mark-recapture experiments, was less than 1000 m (without considering the effect of wind), and of which 90% mosquito species were within 100 m (Byran et al., 1991). It has been suggested that the general flight distance of over 80% mosquito species could be treated as a standard distance (100 m) to study the mosquito activities (Knight et al., 2003; Verdonschot and BesseLototskaya, 2014). Due to not identifying mosquito species in this study (see 3.3. Mosquito density measurement), the authors wanted to calculate landscape composition in a larger area to establish a reliable relationship between landscape variables and mosquito density, because the collected mosquitoes in this study might include some species which could voluntary dispersal more than 100 m. Thus each site was designed as a circle with a radius of 300 m centered on the mosquitotrapping point, which led to two pairs of sites with partially overlapping coverage (see Fig. 1). Despite partial overlaps of two pairs of sites, their landscape compositions still had significant heterogeneity. Each site covered an area of 28.26 ha, and overall areas of the ten sample sites were about 262.09 ha.
of the vector ecology and microtopographic distribution of mosquitoes. These findings seem to suggest that landscape patterns impact on mosquito population. The previous works, however, mainly described the mosquito habitats qualitatively according to field identification (Brown et al., 2008a), a more systematically and definitely quantitative landscape analysis is needed to properly assess the impact of the landscape composition on mosquito population, and provide evidence to guide anti-mosquito landscape design. The satellite remote sensing can provides a cost-effective method to quantify the landscape composition of mosquito’s habitats (e.g. Impoinvil et al., 2008; Severini et al., 2008; Chang et al., 2009). At macro-scale, many literatures indicated that the distribution of mosquitoes was affected by land use types (Junglen et al., 2009), urbanization process (Ferreira and Alecrim, 2004), spatial distribution of water area (Hassan and Onsi, 2004) and geographical landscape (Medeiros-Sousa et al., 2017). Most mosquitoes lived in the residential areas with high human density (Junglen et al., 2009), especially the villages and the slums with poor environmental quality (Hassan et al., 2013). In urban environments, previous studies suggested that the larger the green area and the higher the degree of patch separation, the more the mosquito abundance; also, the most heterogeneous landscape hid the richest number of mosquitoes (Brown et al., 2008a; Chaves et al., 2011; Medeiros-Sousa et al., 2017). The accurate target vector control requires the fine-scaled study in urban environments (Little et al., 2017a). At micro-scale, however, there is limited research on the distribution of mosquitoes in urban green spaces (Landau and Van Leeuwen, 2012). Although, a few studies found that plant diversity, impervious surfaces, the number of structures, medium height trees, soil, open spaces and vacant lots were closely related with mosquito density (Landau and Van Leeuwen, 2012; Little et al., 2017b; Yang, et al., 2019), none of these studies proposed the availably systematic methods for mosquitoes control. Furthermore, a small amount of investigations can not provide reliable guidance for the anti-mosquito design strategy in urban green spaces. Lõhmus and Balbus (2015) claimed that we should pay a great effort to prevent mosquito breeding in the phrase of design, rather than after the occurrence of mosquitoes in urban green spaces.
3.2. Landscape composition measurement Google Earth has been employed in public health monitoring in recent years (Boulos et al., 2011; Liang et al., 2018). A global satellite image at spatial resolutions from Landsat (15–30 m) to QuickBird (60 cm) carries by Google Earth have been released freely to the public, and could be used without any technical barriers (Liang et al., 2018). The present study used Google Earth at the level 20 to acquire remotesensing images of the ten sites for landscape composition measurement (Chang et al., 2009; Xie et al., 2019). The ten images were photographed on 23 June 2018, which were the nearest images we could find to the time when the experiments were conducted (from July to August 2019). Therefore, field observation by the authors was performed to validate the images, and the authors did not find identifiable changes between the field observation and the satellite images on the ten sites. Finally, landscape composition were classified into four types: (1) vegetation which included three subtypes (grass, woods, and aquatic plants); (2) water which included two subtypes (lake and stream); (3) man-made elements which included three subtypes (buildings, squares and roads); and (4) others which included one subtype (bare land) (see Table 1). In order to quantify landscape composition, a grid of 2826 squares (10 m × 10 m) was covered on the image of each site using the Photoshop software. Any subtype that covered more than half of a square was marked and computed (Nordh et al., 2009; Zhao et al., 2013a). At last, the proportion of the landscape occupied by each habitat type was figured out.
2. Aims and overall framework of the study In order to encourage more people contact with green spaces, reduce mosquito nuisance and provide a specific guideline for anti-mosquito landscape design, the present study used Google Earth to obtain data of landscape composition of ten sample sites of urban green spaces in Xuzhou, eastern China. Meanwhile, the mosquito density on the ten sites was measured using mosquito traps. The following questions guided this study. (1) What is the relationship between mosquito density and landscape composition in urban green spaces? (2) How to manipulate the landscape composition to control mosquito in landscape design and management? 3. Methods 3.1. Study areas and sample sites This study was carried out in Xuzhou, Jiangsu Province, eastern China (16°22′−118°40′ E; 33°43′−34°58′ N). Xuzhou is a mediumsized city. Its climate is a typical temperate monsoon with a hot and humid summer (from May to September), and mosquito population peaks in July and August (Li et al., 2016a), because the season is suitable for mosquito survival and reproduction. According to local mosquito surveillance reports (Li et al., 2016a; XZCDC, 2018), the common mosquito species in this area include Culex pipiens pallens, Culex tritaeniorhynchus, Anopheles sinensis, and Aedes albopictus, among which Culex pipiens pallens is the dominant species, accounting for more than 60% of
3.3. Mosquito density measurement In order to remove the occasional factors as far as possible, such as wind, temperature, humidity on each site, the collection of mosquitoes 2
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Fig. 1. Ten sample sites distributed in the study area.
and turned on at the beginning of the evening twilight (19:30), after 2.5 h, the mosquitoes (including dead and alive) were collected and numbered.
Table 1 Landscape composition classes. Type
Vegetation
Water
Man-made elements
Others
Subtype
Grass Woods Aquatic plants
Lake Stream
Buildings Squares Roads
Bare lands
3.4. Statistical analysis SPSS 17 software was employed to analyze the data. After the normality of the data were verified (only the data of Site F had not a normal distribution (Sig. = 0.001), others had normal distributions (Sig. > 0.05)), the One-way ANOVA was used to determine the differences of mosquito density among the ten sites, and the homoscedasticity of the data was also tested. Furthermore, LSD was used for pairwise comparison. Then, the correlations analysis was conducted to explore the relationships between mosquito density and landscape composition, and the stepwise multiple linear regression was used to find the significant drivers of landscape composition for mosquito density.
was performed six times, from July 15 to August 24, 2019 with a fiveday interval, and the actual dates were shown in Table 2. Two collection dates of July 26, August 19 were postponed due to rain or strong wind on the scheduled capturing days (see Table 2). In this study, adult mosquitoes were captured by CDC (Centers for Disease Control) light traps (Silver, 2007). The effectiveness of the equipment has been evidenced by previous works (Wheeler et al., 1996; Li et al., 2016b; Hast et al., 2019). All traps were almost the same height (1.2 m above the ground). The location of trap on each site should be relatively open and dark to avoid the trap lighting was blocked by objects such as tree branches and interfered by existing lighting. These traps were emptied
Table 2 Number of mosquitoes captured by six times on ten sites.
First capture Second capture Third capture Fourth capture Fifth capture Sixth capture Total
July 15 July 26 July 31 Aug 05 Aug 19 Aug 24
Site A
Site B
Site C
Site D
Site E
Site F
Site G
Site H
Site I
Site J
14 3 5 10 2 22 56
3 4 8 10 4 15 44
15 6 14 19 14 38 106
8 1 2 6 3 2 22
5 1 0 3 1 0 10
33 9 8 9 5 5 69
52 27 62 52 54 130 377
42 23 36 30 12 28 171
7 5 10 7 6 0 35
28 28 46 48 23 59 232
3
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Table 3 Percentage of area covered by landscape composition variables on ten sites.
Site Site Site Site Site Site Site Site Site Site
A B C D E F G H I J
Grass (%)
Woods (%)
Aquatic plants (%)
Total vegetation (%)
Lake (%)
Stream (%)
Total water (%)
Buildings (%)
Squares (%)
Roads (%)
Total man-made elements (%)
Bare land (%)
19.43 30.6 15 13.2 5.58 16.49 9.09 3.22 14.23 7.4
34.82 27.92 20.59 74.24 90.27 63.13 30.47 15.75 37.47 9.02
1.34 1.2 0.05 1.2 0 2.09 7.43 5.8 3.4 0.96
55.59 59.72 35.64 88.64 95.85 81.71 46.99 24.77 55.1 17.38
4.71 9.02 0.81 4.25 0 4.78 37.79 60.62 31.92 76.22
2.87 0 2.12 0 0 0 0 1.52 0 0
7.58 9.02 2.93 4.25 0 4.78 37.79 62.14 31.92 76.22
11.04 7.23 12.81 0.42 1.85 0.39 2.69 5.38 0.42 0.28
1.88 6.97 11.89 0.57 0.28 3.11 2.94 1.27 5.84 3.68
10.53 10.58 12.17 6.12 1.52 10.01 10.79 6.44 6.72 2.44
23.45 24.78 36.87 7.11 3.65 13.51 16.42 13.09 12.98 6.4
13.38 6.48 24.56 0 0.5 0 2.8 0 0 0
4. Results
analysis only checked the relationship between mosquito density and landscape composition variables individually. Previous works claimed that the regression analysis could solve this problem (Zhao et al., 2013b). The stepwise multiple linear regression analysis, mosquito density as dependent and landscape composition variables as independents, showed that the amount of aquatic plants is the reliable predictor of mosquito density (Table 4). The validity of the model shown in Table 5 was tested. The normality of the residuals was demonstrated by using the KolmogorovSmimo test (Kolmogorov-Smirnov Z = 0.639; p = 0.809). Variance analysis result revealed a linear correlation between the landscape composition variables and the mosquito density (F = 7.583, p = 0.026). Because of this model only included one predicator, there was no problem with multi-collinearity. Thus, this model was accepted.
4.1. Overall number of adult mosquitoes The number of mosquitoes captured by six times on ten sample sites was shown in Table 2. A total of 1122 adult mosquitoes were collected, in which the highest mosquito population was captured on Site G, and the lowest on Site E. The one-way ANOVA showed that there was a significant difference of mosquito population among the ten sites F = 12.099; p < 0.001; Levene’ Sig. = 0.08, and 24 of 45 pairwise comparisons were significantly different. These results implied that environmental features had an essential effect on mosquito density. 4.2. Relationships between landscape composition and mosquito density
5. Discussion
The measured proportion of landscape composition for each site was presented in Table 3. The mean mosquito numbers of six captures were used as mosquito density on each site. The correlation analysis indicated that mosquito density increased with the increase of aquatic plants, lake and total water coverage (Table 4). Despite of the fact that there were significantly linear relations among landscape composition variables (see Table 4), the correlation
5.1. Aquatic plants in relation to mosquito density The study indicates that the amount of aquatic plants is the reliable predicator of mosquito density. This result replicates the finding of existing works (Walton et al., 2012; Yadav et al., 2012; Lõhmus and
Table 4 Correlations between mosquito density and landscape composition variables (Pearson).
Mosquito density Grass Woods Aquatic plants Total vegetation Lake Stream Total water Buildings Squares Roads Total man-made elements
Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed) Coefficient Sig.(2-tailed)
Grass
Woods
Aquatic plants
Total vegetation
Lake
Stream
Total water
Buildings
Squares
Roads
Total manmade elements
Bare lands
－0.405 0.246
－0.552 0.098 －0.067 0.855
0.694* 0.026 －0.337 0.341 －0.321 0.366
－0.614 0.059 0.201 0.579 0.961** 0.000 －0.332 0.349
0.652* 0.041 －0.520 0.123 －0.644* 0.045 0.500 0.141 －0.760* 0.011
－0.074 0.839 0.065 0.859 －0.362 0.305 －0.103 0.778 －0.356 0.313 －0.157 0.665
0.652* 0.041 －0.520 0.123 －0.662* 0.037 0.499 0.142 －0.778** 0.008 0.999** 0.000 －0.117 0.747
－0.083 0.820 0.387 0.269 －0.406 0.244 －0.205 0.570 －0.314 0.377 －0.332 0.349 0.851** 0.002 －0.300 0.400
－0.007 0.985 0.449 0.193 －0.492 0.148 －0.239 0.507 －0.385 0.271 －0.149 0.682 0.214 0.552 －0.141 0.698 0.535 0.111
0.124 0.732 0.609 0.062 －0.317 0.372 0.206 0.568 －0.119 0.743 －0.387 0.269 0.426 0.219 －0.372 0.290 0.621 0.056 0.545 0.103
0.005 0.990 0.561 0.092 －0.478 0.162 －0.104 0.774 －0.325 0.359 －0.348 0.324 0.627 0.052 －0.325 0.360 0.880** 0.001 0.800** 0.005 0.846** 0.002
－0.076 0.834 0.367 0.297 －0.326 0.357 －0.351 0.319 －0.253 0.481 －0.420 0.227 0.735* 0.015 －0.393 0.261 0.910** 0.000 0.714* 0.020 0.627 0.052 0.904** 0.000
*Significance at the 0.05 level; **Significance at the 0.01 level. 4
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Table 5 Significant predictor for the mosquito density in urban green spaces emerging from stepwise multiple linear regression analysis. Dependent
Mosquito density (R² = 0.482; adjusted R² = 0.418)
Independent
(constant) Aquatic plants
Unstandardized Beta
35.576 32.648
Standardized Beta
0.694
T
0.895 2.730
Significance
0.397 0.026
Collinearity statistics Tolerance
VIF
1.000
1.000
suitable for mosquitoes to live in. At last, mosquitoes have a behavioral bias named appetitive flight for host seeking (Service, 1997), and the female mosquitoes are bloodthirsty, thus mosquito population is generally related to the amount of host (Verdonschot and Besse-Lototskaya, 2014; Heinisch et al., 2019). In urban green spaces, humans are more likely to be the potential hosts because only a few animals live in urban green spaces (Halasa et al., 2014; Lounibos and Kramer, 2016). But vegetation, especially the woods, is not conducive to human access, especially during the evening. That is to say mosquito density is lower in woods due to less potential hosts.
Balbus, 2015). Aquatic plants have some favorable features for mosquito life. Firstly, aquatic plants signify a water area, which indicate not only enough plant juices but also water resource, implying that the local mosquitoes do not need risk-laden active dispersal from energyintensive areas to further places (Wong and Jim, 2018). Secondly, the dense stems of aquatic plants tend to intercept flowing water and create stagnant water (Greenway, 2003) which has been evidenced a major factor related to more mosquito larvae (Russell, 1999; Yadav et al., 2012), because a relatively static water area can prevent mosquito eggs from being washed away (Batzer and Resh, 1992). Thirdly, the predators as biological agents in controlling mosquito population have been widely reported (Knight et al., 2003; Kumar and Hwang, 2006; Dale et al., 2007). Aquatic plants colony is not conducive to the predators entering the interior of the plant community to hunt due to the combination of dense aquatic plants and the accumulation of decaying plant materials (Walton, 2007; Walton et al., 2012). And, low dissolved oxygen accompanied by intensive aquatic plants in water is another factor of inhabitation of predators survival (Thullen et al., 2002), but does not affect the mosquitoes (Nelson and Thullen, 2008; Lõhmus and Balbus, 2015).
5.4. Application for mosquito control in urban green spaces In order to cut down mosquito population in urban green spaces, in turn mitigating the nuisance and reducing vector-borne infectious diseases, this study give a clear clue to guide landscape design: reducing aquatic plants and water area, and setting more woodland in urban green spaces. In practice, it needs to pay more attention to the drainage design of rainfall to avoid the presence of temporary water area in rainy season. Maybe, the most economical way is to increase the permeability of the ground, this method also contribute to urban sustainable development (Zhou et al., 2018). Concerning the fact that aquatic plants are the solely predicator of mosquito density, we should cut down the percentage of water covered by aquatic plants in urban green spaces. However, designers have to consider the fact that the main aquatic plants in the ten sites studied are Nelumbo nucifera, Nymphaea tetragona, Lemna minor, Phragmites communis, Iris pseudacorus, and Typha orientalis. It is unclear whether other species contribute to mosquito population. Meanwhile, woody plants are encouraged in urban green spaces. They not only have more ecological values than grass, but also can decrease the mosquito population. At last, finding mosquito’s predators is never the aim of this study, but biological enemies such as water boatmen, backswimmers and mosquito fish can be used to control mosquito population (Knight et al., 2003; Kumar and Hwang, 2006; Dale et al., 2007). For this issue, we have to create suitable habitats to support the predators’ survival and reproduction. When applying these results to practice, we must keep in our minds that urban green spaces bear multiple functions such as aesthetic, recreation and ecological sustainability. It should be noted that mosquitoes control as one of the design goals is not always consistent with other appeals. For example, according to the results of this study, waterscape will increase mosquito density, however, the waterscape in urban areas claims a high rating of visual aesthetic quality (Herzog and Barnes, 1999; Kaltenborn and Bjerke, 2002; Lee, 2017;) and positive effects on mental stress relief of visitors (Zhao et al., 2018). Similarly, despite the fact that aquatic plants were positively associated with mosquito density (Table 5), they have an important contribution to ecological service such as water purification (Chambers et al., 2008; O’Hare et al., 2017; Lu et al., 2018). Medeiros-Sousa et al. (2015) suggest that the complete removal of mosquito habitats possibly causes the risks of ecological degradation. Thus, landscape design has to reach a balance among the multiple functions provided by a landscape, and maximize the comprehensive benefits. However, mosquito control is generally ignored by landscape architects. This study suggests that we should take mosquito control into account in the design process of urban green spaces.
5.2. Water in relation to mosquito density It is well-known that water is necessary for mosquito breeding. In urban green spaces, most mosquito species live in permanent water body such as vegetated lakes, ponds, creeks and rivers (Crocker et al., 2017), or temporary water body such as puddles formed after rainfall or irrigation (Russell, 1999; Medeiros-Sousa et al., 2015). Therefore, the presence of water can support mosquito survival and reproduction. This result is also demonstrated by the present study which suggests that there is a statistically positive correlation between water area and mosquito density (see Table 4). Biologically, mosquito is completely metamorphosed insect. Its life consists of four stages: egg, larva, pupa and adult, of which the first three stages need grow in water. Although adult male mosquitoes migrate to plant communities on land, female adult mosquitoes still live in the appropriate water or moist settings as the spawning base after mating. Thus, the larger water area in a landscape is, the more mosquitoes live in. Obviously, it also shows us a clear clue that the key to reduce mosquito density is “water control”. 5.3. Land vegetation in relation to mosquito density Despite some benefits that plant community provides to mosquito, such as sugar sources and shelters (Foster, 1995；Brown et al., 2008b; Hayden et al., 2010), this study concludes that land vegetation is not frequented by mosquitoes in urban green spaces (Table 4). This result possibly is explained by the following three reasons. Firstly, the crown of trees can retain a part of rainfall, and the natural soil under the trees penetrates rain water more quickly than pavements (Dunne et al., 1991; Sarr et al., 2001; Harden and Scrugges, 2003), which cause less surface water in forest and less static water such as puddles to support mosquito breeding. Secondly, Junglen et al. (2009) concludes that plant height has a major impact on mosquito population, the taller the plants, the fewer mosquitoes. The finding of this study generally supports it (see Table 4, the mosquito density has a closely significantly negative relationship with woods). It can be speculated that tall vegetation is not 5
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5.5. Limitations and future research
influence the work reported in this paper.
Firstly, the trapped mosquitoes were not classified by species and sex in our study. In fact, different habitats can support different mosquito species (Gleiser and Zalazar, 2009; Landau and Van Leeuwen, 2012) which maybe differ in epidemiological significance (Xia et al., 2018), and only female mosquitoes need blood meals, which are the culprits of the nuisance and risk of infection (Diagana et al., 2007). Therefore, in a future study, researchers need to find the species composition of mosquito on each sample site, the most popular species should be paid more attention. Meanwhile, the female mosquitoes should be identified and their preferred habitats should be regarded as the priority intervention project in the design and management of urban green spaces. Secondly, a full test of population density requires the detection of larvae (Medeiros-Sousa et al., 2015, 2017; Maimusa et al., 2016; Heym et al., 2018; Heinisch et al., 2019), our experiments, however, only collect the adult mosquitoes. Although immature mosquitoes can not cause nuisance and spread diseases, their density can predict the amount of adult mosquitoes. If the design of anti-mosquito landscape can focus on controlling the larvae, maybe, it is more efficient than focusing on the adults (Wong and Jim, 2018). Thirdly, many scholars have suggested that meteorological factors such as temperature, humidity and wind also play a role in mosquito aggregation (Daszak et al., 2000; Reiter, 2001; Reiter and Lapointe, 2007; Codeço et al., 2015). And different sites in urban green spaces can form different microclimates (Wong and Jim, 2017; Yang et al., 2019a,2019b), which possibly affect the mosquito density on the sites. However, this present study does not take the microclimate into account. There are few reports to answer the question of how to use landscape design to influence microclimate and further affect the mosquito density, it is necessary to conduct researches to explore this issue in the future, which maybe provide practical guidance to antimosquito landscape design. At last, this study concluded that aquatic plants and water have a significant relationship with mosquito density. However, aquatic plants and water possess some specific features, such as plant morphology, species composition, configuration of aquatic plants on the water, and the size, quality, shape and depth of water. These features possibly contribute differently to mosquito control. Further research is needed to investigate the extent to which aquatic plants and water function as mosquito habitats in urban green spaces, which will deepen our finescaled understanding on the relationships between environmental features and mosquito population and provide practical guidance to landscape design.
Acknowledgement This research is supported by the Fundamental Research for the Central Universities (2017WB09). References Aronson, M.F.J., Lepczyk, C.A., Evans, K.L., Goddard, M.A., Lerman, S.B., MacIvor, J.S., Nilon, C.H., Vargo, T., 2017. Biodiversity in the city: key challenges for urban green space management. Front. Ecol. Environ. 15, 189–196. Batzer, D.P., Resh, V.H., 1992. Wetland management strategies that enhance waterfowl habitats can also control mosquitoes. J. Am. Mosq. Control Assoc. 8, 117–125. Boulos, M.N.K., Resch, B., Crowley, D.N., Breslin, J.G., Chuang, K.Y.S., 2011. Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. Int. J. Health Geogr. 10, 67. Branas, C.C., Cheney, R.A., MacDonald, J.M., Tam, V.W., Jackson, T.D., Ten Have, T.R., 2011. A difference-in-differences analysis of health, safety, and greening vacant urban space. Am. J. 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6. Conclusion This study explores the effects of landscape composition on mosquito density in urban green spaces. It expects that landscape architects consider mosquito ecology in the design stage of urban green spaces, and use design strategy to cut down mosquito density. The results claim that more aquatic plants and higher water coverage in a landscape indicate higher mosquito density. Although there is a non-significant relationship between woody plants and mosquito density, generally speaking, woody plants are not conductive to mosquito development. Despite of some limitations, these results inform landscape architects that they can control mosquitoes in urban green spaces by manipulating the landscape composition variables which have a significant impact on mosquito population. For example, reducing the amount of water, controlling aquatic plants, and planting more woods in a landscape are efficient ways to decrease mosquito population. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to 6
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