Anthropogenic landscape decreases mosquito biodiversity and drives malaria vector proliferation in the Amazon rainforest

RESEARCH ARTICLE Anthropogenic landscape decreases mosquito biodiversity and drives malaria vector proliferation in the Amazon rainforest Leonardo Suveges Moreira ChavesID1*, Eduardo Sterlino BergoID2, Jan E. Conn3,4, Gabriel Zorello LaportaID5, Paula Ribeiro Prist6, Maria Anice Mureb Sallum1‡ 1 Departamento de Epidemiologia, Faculdade de Sau´ de Pu´ blica, Universidade de São Paulo, São Paulo, SP, Brazil, 2 Superintendência de Controle de Endemias, Secretaria de Estado da Sau´de de São Paulo, Araraquara, SP, Brazil, 3 Wadsworth Center, New York State Department of Health, Albany, NY, United States of America, 4 Department of Biomedical Sciences, School of Public Health, State University of New York, Albany, NY, United States of America, 5 Setor de Po´s-graduac¸ão, Pesquisa e Inovac¸ão, Centro Universita´rio Sau´ de ABC, Fundac¸ão ABC, Santo Andre´, SP, Brazil, 6 Department of Ecology, Institute of Bioscience, University of São Paulo, São Paulo, SP, Brazil ‡ Senior author. * leonardosuveges@usp.br Abstract Inter-relationships among mosquito vectors, Plasmodium parasites, human ecology, and biotic and abiotic factors, drive malaria risk. Specifically, rural landscapes shaped by human activities have a great potential to increase the abundance of malaria vectors, putting many vulnerable people at risk. Understanding at which point the abundance of vectors increases in the landscape can help to design policies and interventions for effective and sustainable control. Using a dataset of adult female mosquitoes collected at 79 sites in malaria endemic

areas in the Brazilian Amazon, this study aimed to (1) verify the association among forest cover percentage (PLAND), forest edge density (ED), and variation in mosquito diversity; and to (2) test the hypothesis of an association between landscape structure (i.e., PLAND and ED) and Nyssorhynchus darlingi (Root) dominance. Mosquito collections were per- formed employing human landing catch (HLC) (peridomestic habitat) and Shannon trap combined with HLC (forest fringe habitat). Nyssorhynchus darlingi abundance was used as the response variable in a generalized linear mixed model, and the Shannon diversity index (H’) of the Culicidae community, PLAND, and the distance house-water drainage were used as predictors. Three ED categories were also used as random effects. A path analysis was used to understand comparative strengths of direct and indirect relationships among Ama- zon vegetation classes, Culicidae community, and Ny. darlingi abundance. Our results dem- onstrate that Ny. darlingi is negatively affected by H´ and PLAND of peridomestic habitat, and that increasing these variables (one-unit value at β0 = 768) leads to a decrease of 226 (P < 0.001) and 533 (P = 0.003) individuals, respectively. At the forest fringe, a similar result was found for H’ (β1 = -218; P < 0.001) and PLAND (β1 = -337; P = 0.04). Anthropogenic changes in the Amazon vegetation classes decreased mosquito biodiversity, leading to increased Ny. darlingi abundance. Changes in landscape structure, specifically decreases in PLAND and increases in ED, led to Ny. darlingi becoming the dominant species,

PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.0245087 January 14, 2021 1 / 25 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Chaves LSM, Bergo ES, Conn JE, Laporta GZ, Prist PR, Sallum MAM (2021) Anthropogenic landscape decreases mosquito biodiversity and drives malaria vector proliferation in the Amazon rainforest. PLoS ONE 16(1): e0245087. https://doi. org/10.1371/journal.pone.0245087 Editor: Deborah Faria, Universidade Estadual de Santa Cruz - UESC, BRAZIL Received: October 30, 2020 Accepted: December 21, 2020 Published: January 14, 2021 Copyright: © 2021 Chaves et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: This work was supported by Fundac¸ão de Amparo à Pesquisa do Estado de São Paulo (FAPESP; grants 2014/26229-7, 2014/26855-5 and 2014/09774-1), and by National Institutes of Health, USA, grant 2R01AI110112-06A1 to JEC. Competing interests: The authors have declared that no competing interests exist.

increasing malaria risk. Ecological mechanisms involving changes in landscape and mos- quito species composition can help to understand changes in the epidemiology of malaria. Introduction Various interconnected mechanisms associated with deforestation and land-use, such as envi- ronmental change, abundance and size of deforested patches, and biodiversity loss can alter human exposure to many infectious diseases, especially malaria [1–5]. The relationship between biodiversity and its capacity to protect humans from infectious diseases has been widely debated and, as suggested by Johnson et al. [6], the central discussion is how ecological interactions among hosts, parasites and environmental factors change diversity and disease risk. The dilution effect hypothesis (DEH) supports the premise that high biodiversity dilutes the infection sources represented by reservoir species and reduces disease risk [7, 8]. As a corollary to the DEH, pathogen competent hosts are frequently found in low diversity settings [9–11]. The diffuse competition hypothesis (DCH), posits that the high diversity of non-vector mos- quitoes blood feeding on a few vertebrate species may increase the defensive behavior of the host, thus decreasing the number of bites and the risk of exposure to infective vectors [12, 13]. Laporta et al. [3] found a strong association among reduced malaria risk, increased diversity of the mosquito community caused by the augmentation in DCH and high diversity of sylvatic vertebrates, linked to increased DEH.

The DEH was supported in an empirical test of mosquito-borne diseases in Panama [14]. In the malaria endemic Amazon basin, the dilution effect is highly heterogeneous due in part to differential drivers of human behavior, economic, cultural, and social factors, which can influence malaria risk [15, 16]. The Plasmodium life cycle requires highly specific inter-rela- tionships among humans, mosquitoes, and parasites [17]. We propose that anthropogenic changes in a native forest environment, such as forest fragmentation, will modify patterns of Plasmodium parasite activity, representing expansion of a malaria risk area, and a challenge for malaria control. Forest fragmentation increases ecotone habitats [18, 19], causing changes in the spatiotem- poral abundance of anopheline larval habitats [20–23], increased vector abundance [24–27] and malaria risk [4, 28–31]. According to the deforestation-malaria link, defined by Sawyer [32] and de Castro et al. [16] as “frontier malaria”, forest clearing increases the average temper- ature in agricultural settlements and in neighboring larval habitats. Temperature increase can lead to a decrease in the larva to adult development time, increase in adult survivorship and in vectorial capacity of mosquito populations [21, 33–35]. Moreover, economic development is associated with the exploitation of forest commodities and timber, agriculture, and cattle ranch expansion [36], and as such, land occupation requires continuous human movement within the region. Such human movement promotes propaga-

tion of Plasmodium to new areas, and persistence in others where the disease is decreasing, as demonstrated in Burkina Faso, Africa [37]. The dynamics of socioecological change, poor housing, inadequate access to water and sanitation, and lack of or poor access to health facili- ties by the inhabitants also serve to intensify malaria transmission [38]. Nyssorhynchus darlingi (Root) is the primary vector in Amazonian Brazil [39, 40]. Both the occurrence and increased abundance of this species in malaria landscapes have been associated with spatial and temporal distribution of aquatic habitats for female oviposition and the PLOS ONE Anthropogenic landscape and mosquito biodiversity drives malaria vector proliferation PLOS ONE | https://doi.org/10.1371/journal.pone.0245087 January 14, 2021 2 / 25 Abbreviations: PLAND, forest cover percentage; ED, edge density; HLC, human landing catch; ST, Shannon trap using light and human attraction; GLMM, generalized linear mixed model; H’, Shannon diversity index; DEH, dilution effect hypothesis; DCH, diffuse competition hypothesis; DW, water drainage network; DEM, Digital Elevation Model; PCA, Principal Component Analysis; SEM, Structural Equation Model; CCA, Canonical correspondence analysis; Atp, Anthropogenic; OTMF, Open Tropical Moist Forest; OTMFA, Open Tropical Moist Forest with Arecaceae dominance; TMF, Tropical Moist Forest; Cpn, Campinarana.

development of immature stages [20, 41]. Aquatic habitats include anthropogenic and natural water bodies such as freshwater streams, small forest rivers (igarape´s), and partially shaded fishponds, usually located along the forest border [34]. Continuous changes in the landscape also influence the spatiotemporal dynamics of the malaria vectors, that normally exhibit sea- sonal peaks in abundance depending on the environmental distribution and availability of habitats in the wet-dry and dry-wet transition seasons [42–44]. Anthropogenic changes in nat- ural environments either can increase the distribution and availability of standing water despite season or increase the duration of habitats, thus diminishing the seasonal impact on the dynamics of vector populations (Rohr et al. 2019). Culicidae diversity and landscape composition or configuration, distinct patterns of land occupation and land-use have all been considered important drivers of malaria, changing the biodiversity of ecosystem services [3], shifting primary vector species [24, 45, 46], and exposing the human population to risk [47]. In endemic areas, a decrease in Anophelinae diversity increases both the risk of human exposure to vector mosquitoes and the likelihood of acquir- ing malaria, especially in areas where the landscape structure is conducive to vector-human contact [24, 27, 48]. In Amazonian rural landscapes, malaria risk is dynamic and associated with a complex network of environmental changes, ecological, entomological, socioeconomic, and political determinants [20, 49, 50].

The source-sink dynamics concept was originally proposed to describe variation in habitat quality that can affect population growth or decline for a target species [51]. More recently, this concept was embraced by the field of landscape ecology [52] and has joined the theory of island biogeography. Immigration and extinction rates across a heterogenous habitat com- posed of continuous forest (source) and small forest patches (sinks) in an anthropogenic matrix define the persistence of a given species. In the case of mosquitoes, the source-sink dynamics can be applied to understand and predict transmission of sylvatic pathogens to humans, or vice-versa. This study aims to better understand the effects of change in the diversity, community structure and composition of mosquito species in a continuous range of landscape elements, i.e., percent forest cover and forest edge density configurations, in endemic malaria areas in Amazonian Brazil. We hypothesized that (1) decreased forest cover and increased forest edge density lead to a change in mosquito diversity; (2) Ny. darlingi dominance is dynamic and highest in areas where forest cover is actively being decreased by human occupation. Methods Study area Mosquitoes were collected in 79 sampling units in 12 municipalities in the Brazilian Amazon states of Acre (twice in Acrelaˆndia, Cruzeiro do Sul, Maˆncio Lima, Rodrigues Alves), Amazo- nas (Itacoatiara, Guajara´, Humaita´, La´brea, São Gabriel da Cachoeira), Para´ (Pacaja´), and Ron-

Each of the 79 sampling units was centered on a house around which was subtended a circle of 1 km radius (3.1 km2). Each unit consisted of one peridomestic habitat and one forest edge habitat (Fig 2) and was separated from all other units by approximately 2.25 km [56]. There were six sampling units per locality, except for São Gabriel da Cachoeira, Amazonas state, which had seven. Fig 1. Map of Brazilian Amazonian biome (in red) in South America. A: Ko¨ppen and Geiger Climate Classification; Af–Tropical Rainforest, Am–Tropical Monsoon, As–Tropical wet and dry, Aw–Savanna, Cfa–Humid subtropical, Cwa–Subtropical-dry winter. B: Brazilian Vegetation Class (IBGE, 2012); Anthropogenic–dominance of Poaceae, Verbanaceae, Lamiaceae, Solanaceae and Asteraceae botanical families; Tropical Moist Forest–dominance of Lecythidaceae and Vochysiaceae; Tropical Moist Forest Arecaceae–dominance of Lecythidaceae, Vochysiaceae and Arecaceae; Riverine and Marine Vegetation–Arecaceae, Solanaceae, Myrtaceae and Rhizophoraceae; Campinarama–Euphorbiaceae, Arecaceae, Humiriaceae and Fabaceae; Savana–Poaceae, Cyperaceae, Amarylidaceae and Xyridaceae; Seasonal Lowland Forest–Lauraceae, Lythraceae, Boraginaceae, Fabaceae and Myrtaceae. C: Amazonian ecoregions based on biogeography (https:// ecoregions2017.appspot.com/) [100]. D: Quarterly Totals Precipitation (mm3) of June, July and August from 1977 to 2006 (http://cprm.gov.br). Reprinted from QGIS version 2.8 without any changes, under a CC BY license, with permission from PLOS ONE, original copyright 2020.

https://doi.org/10.1371/journal.pone.0245087.g001 Fig 2. Study design scheme. Large circle (red) represents the sampling unit, and smaller circles (blue) the collection points. A: peridomestic habitat (HLC), and B: forest fringe habitat (ST). https://doi.org/10.1371/journal.pone.0245087.g002 PLOS ONE Anthropogenic landscape and mosquito biodiversity drives malaria vector proliferation PLOS ONE | https://doi.org/10.1371/journal.pone.0245087 January 14, 2021 4 / 25

Mosquito collection Collections were conducted outdoors in the peridomestic habitat (approximately five meters from the household entrance) using human landing catch (HLC) and in the forest fringe habi- tat (approximately 10 meters into the forest interface with open areas) with Shannon trap using light and human attraction (ST) [57]. There were two collection points for each sampling unit and HLC and ST collections were conducted simultaneously. Mosquito data from HLC and ST were summed and employed as the field data representative for each sampling unit. Therefore, the total of mosquitoes collected on HLC and ST were evaluated together and used as fixed variable in the models at 1 km radius for each sampling unit. Sampling was performed once between 18:00 and 0:00h, from April to November 2016–17, during the wet-dry transition, dry season, and dry-wet season (S1 Table in S1 File). The only exception to the simultaneous collections was during heavy rain and /or strong wind. Mosqui- toes were collected with a manual vacuum aspirator. Two collectors worked each night inn each habitat, using HLC in the peridomestic area and ST in the forest fringe; each unit was sampled once. Variation in the number of collectors was due to their availability during field trips. Every hour, mosquitoes were euthanized with ethyl acetate (C4H8O2) vapors in the field and stored in silica gel identified by sampling unit number, habitat, date, and hour for subse- quent species identification using the morphological identification key of Forattini [58]. The

latitude and longitude of each collection point was recorded in the field with a GPS unit, using geodetic, global geographic coordinate WGS84. The decision for collecting mosquitoes with ST at the forest fringe habitat considered the effectiveness of the trap for sampling Culicidae diversity [59]. These two collection methods have been compared previously in the Amazonian forest, and abundance and presence of Ny. darlingi were similar [60]. The sampling effort for each collection method and habitat was the same. Landscape metrics Landscape metrics were generated from a Sentinel 2A satellite imagery (European Space Agency (ESA)) from the day nearest to the date of collection. with a spatial resolution of 10 m. Selection of the images included 1) the lowest percentage of clouds, and 2) as near as possible to the field collection date. The images were downloaded from the ESA (https://www.esa.int/ ESA) in raster format and graded, with radiometric and atmospheric corrections, by means of an algorithm developed by ESA and implemented in Sen2Cor software [61]. The satellite images were classified in two land-use classes (forest and non-forest) through a supervised classification, using the maximum likelihood method. All analyzes were performed in QGIS, version 2.8 without any changes (QGIS is licensed under Creative Commons Attribution- ShareAlike 3.0 license (CC BY-SA) (https://creativecommons.org/licenses/by-sa/3.0/)). Percentage of forest cover (PLAND) and forest edge density (ED) were calculated for all 79

sampling units (1 km radius). Details on how to calculate these metrics can be found in the S1 File [62]. PLAND and ED were also measured on a subtended circle of 0.5 km radius (0.8 km2), measured around each collection point (HLC/ST). These radii were chosen because in a study performed in Malaysian Borneo, Brock and collaborators [63] demonstrated that the optimal radius for addressing the edge effects on P. knowlesi malaria occurrence was 0.5 km, but the proportion of cleared land within 1 km was low. PLAND and ED were chosen because of the ecological characteristics of Ny. darlingi, which have an affinity for partly shaded habitats for oviposition and development of immature stages in ground pools and slow-moving streams at forest edges, as reviewed by Hiwat and Bretas [34]. These metrics were used to assess the effects of landscape elements on mosquito PLOS ONE Anthropogenic landscape and mosquito biodiversity drives malaria vector proliferation PLOS ONE | https://doi.org/10.1371/journal.pone.0245087 January 14, 2021 5 / 25

abundance and: i) the forest cover (PLAND), ii) fragmentation (ED), and iii) the relationship between PLAND and ED (landscape structure). ED measured at the 0.5 km radius was grouped into three categories—low (0–1.5 m / ha x 100); moderate (1.5–2.0 m / ha x 100) and high (2.0–3.5 m / ha x 100) and was always used in posterior analysis as a random effect and as categorical variables (low, moderate and high). These followed results from [56, 63], and the equal distribution of samples categories, with the mosquito collection point as the random effect. To define the ED categories as a random effect, we employed the results of the descriptive analysis that demonstrated differences in mosquito biodiversity in each specific ED category defined here. PLAND and ED measured at every sampling unit (1km radius) were used both as continu- ous and categorical variables. The continuous was also used as a predictor, while the categori- cal entered as a random effect in the analysis. Combinations of PLAND and ED were classified in four categories of landscape (S2 Fig in S1 File) once we decided to test the hypothesis that modification in the landscape structure (herein considered to be a combination of PLAND and ED metrics in each sampling unit area) shifts in mosquito species composition and Ny. darlingi abundance. The categories were: A–PLAND � 50% and ED < 0.015 m/ha; B– PLAND � 50% and ED � 0.015 m/ha; C–PLAND > 50% and ED � 0.015 m/ha; D– PLAND > 50% and ED < 0.015 m/ha. The overview of the sample distribution according to

landscape structure analysis (categories A, B, C, D) is presented in S3 Fig in S1 File. We also measured the distance from each of the 79 sampling units to the nearest water drainage network (DW). Each network was constructed from the Digital Elevation Model (DEM) available on ESA. The spectral bands of each of the images were composed of bands 2 (Blue; 0.49 μm), 3 (Green; 0.56 μm) and 4 (Red; 0.66 μm) to generate a final-colored image for each region of the collection points. The drainage network indicates where water is most likely to accumulate according to the terrain relief but does not necessarily indicate locations of riv- ers or streams. Diversity indices The diversity indices of Margalef, Shannon, Simpson and Berger-Parker for each sampling unit were calculated by the Re´nyi series, based on Hill’s measures [64]. As diversity indexes are highly autocorrelated, we used the Shannon index in analytical associations because it has links to entropy theory [65]. Statistical analysis Data analyses were conducted in three steps: 1) descriptive statistics, and 2) analytical associa- tions. To construct the models, and prior to commencing the analytical approach, we applied extensive descriptive statistics. We assessed the sampling unit distribution according to the landscape structure and vegetation class (S3 Table in S1 File). The units that corresponded to landscape D and Open Tropical Moist Forest (OTMF) showed the lowest mean Ny. darlingi abundance and were considered the baseline group to create dummy variables for generalized

linear mixed model (GLMM) and regression analysis. For step 1, Spearman’s correlation index (at a significance level of 5%) was utilized to verify the correlation among predictor variables (diversity index, PLAND, ED, drainage network (DW), landscape structure—landscape categories A, B, C, D—vegetation class), and number of Ny. darlingi. Principal Component Analysis (PCA) was used in exploratory analyses to iden- tify associations among the number of Ny. darlingi, PLAND, ED and Shannon index variation according to the four categories of landscape structure. The non-parametric Kolmogorov- Smirnov test was employed to verify whether the variables had a normal distribution (at a PLOS ONE Anthropogenic landscape and mosquito biodiversity drives malaria vector proliferation PLOS ONE | https://doi.org/10.1371/journal.pone.0245087 January 14, 2021 6 / 25

significance level of 5%). To compare the distribution of the Ny. darlingi abundance among the different landscape categories and five vegetation classes found in our field collection areas in the Amazon: Anthropogenic (Atp), Open Tropical Moist Forest (OTMF), Open Tropical Moist Forest with Arecaceae dominance (OTMFA), Tropical Moist Forest (TMF) and Campi- narana (Cpn) (Brazilian Institute of Geography and Statistics—IBGE classification) [55], we applied the Kruskal-Wallis with Mann-Whitney-Wilcoxon’s multiple comparison test with Bonferroni correction. The analysis of variance (oneway ANOVA) with Tukey’s corrections was applied to verify Shannon-index variation among landscapes categories, and to test the null hypothesis that landscape structure does not influence species abundance, considering 5% as the significance level. Tukey’s correction was employed to compare the landscape structure categories in five classes of vegetation found in our collection localities. Variation in species composition among sampling units in each landscape structure category was addressed employing Canonical correspondence analysis -CCA in R-project, vegan package [66]. In the step 2, we used a simple linear regression and generalized linear mixed models (GLMM) to verify the relationship between Ny. darlingi abundance and the Shannon index, PLAND, ED and DW. We employed the GLMM statistical analyses because both edge density and landscape categories were random variables. A table with all models can be found in S2

Table in S1 File. In all models we employed PLAND and ED measured at each sampling unit (3.14 km2), as predictor variables, while ED measured at 0.5 km radius (both from peridomes- tic and forest fringe habitats) and classified as categorical variables, was used as a random effect. The two mosquito sampling points (ST and HLC) and their mosquito diversity assem- blages were assessed together as fixed variables. Four simple regression models were con- structed—Shannon index, PLAND, ED and DW. For GLMM, three models were constructed, all of them using Shannon index, PLAND, and DW as fixed effects, and ED categories (low, moderate, high) at peridomestic or forest fringe habitat, or landscape structure categories as random effects. The PLAND and ED metrics were calculated at the 1 km radius and used to categorize landscapes A–D only. For the models (1) and (2), the ED was calculated at the 0.5 km radius for both peridomestic and forest fringe habitats and used as a random effect. The center of the 0.5 km circle of both peridomestic and forest fringe habitats were inside a 1 km radius circle. Mosquito data from peridomestic and forest fringe habitats were combined for the GLMM analysis. The choice of using ED as a random effect were based on the results of the descriptive analysis, which showed a statistically significant difference in Shannon-index (S3 Fig in S1 File) to Culicidae biodiversity. The categorical approach to PLAND analysis did not show differences for Shannon-index variance (P > 0.05).

We used path analysis to test the prediction that changes in Ny. darlingi abundance with increasing Shannon index are the cause of changes in richness (number of different taxonomic mosquito species morphologically and/or molecularly distinct) [58, 64, 67, 68], and to quantify the direct effects on Ny. darlingi abundance caused by variation in the Shannon index, PLAND, ED, landscape structure categories (as dummy variable), vegetation classes (as dummy variable) and DW, while removing the effects of other independent variables [69, 70]. We inserted the vegetation class to verify whether it represents a confusion variable [71]. We employed the Structural Equation Model (SEM) to visualize the model using a path diagram with “semPlot” package of R-project v. 3.6. The GLMM models were adjusted by the distance between the sampling units and drainage networks because of the bionomic characteristics of Ny. darlingi as an ecotone species that lay eggs in partially shaded habitats, including forest fringe [20, 34]. All statistical analysis was car- ried out using RStudio v.1.1.456 software (R Development Core Team, R Foundation for Sta- tistical Computing, Austria) R-project (available at http://www.r-project.org), and “BiodiversityR”, “wordcloud” and “nlme” packages. PLOS ONE Anthropogenic landscape and mosquito biodiversity drives malaria vector proliferation PLOS ONE | https://doi.org/10.1371/journal.pone.0245087 January 14, 2021 7 / 25

Results Mosquito collection In total, 25,323 specimens of 137 species in 17 genera were collected using the HLC and Shan- non trap. Nyssorhynchus darlingi corresponded to 46.6% (11,810 specimens) of this total. Overall, the most abundant species were those in the subfamily Anophelinae, 56.4% (14,281), with Ny. darlingi comprising 82.7% (11,810) of the total Anophelinae collections in the perido- mestic habitat (Table 1). The next most abundant groups were the Mansoniini tribe, compris- ing 19.4% (4,917), Culicini 18.7% (4,743) and Aedini 3.8% (950). Mosquitoes of the Aedeomyiini, Uranotaeniini and Sabethini tribes comprised 1.2% of the total collected (S4 Fig in S1 File). Nyssorhynchus darlingi was the dominant species in all landscape structure categories in the Campinarana vegetation class. The relative abundance of Anophelinae species was high in anthropogenic vegetation class in landscape A (> 70%), but lowest (< 20%) in landscapes B and D. Anophelinae abundance was higher than 50% in all landscape categories in vegetation classes defined as OTMF, OTMFA and TMF, with the exception of landscapes C and D for TMF (< 40%). The distribution of Culicidae groups by landscape structure and vegetation class is shown in the S5 Fig in S1 File. Landscape variables The relationship between PLAND and ED (S6 Fig in S1 File) demonstrates that lower values of ED corresponded to either high or low PLAND percentages, because ED represents fragmen- tation levels in the landscape (r2 = 0.04; F1,77 = 3.2; P = 0.07). Nyssorhynchus darlingi was domi-

nant in the peridomestic habitat (S7A Fig in S1 File); conversely Culicidae diversity was highest in the forest fringe (S7B Fig in S1 File) with the highest density of an individual species being Coquillettidia venezuelensis. The distribution of Culicidae species by ED categories (Fig 3) at the forest fringe demon- strates that Ny. darlingi was dominant in landscapes with a low edge density (0–1.5 m / ha x 100), Mansonia titillans in moderate (1.5–2.0 m / ha x 100), and Cq. venezuelensis in high edge density (2.0–3.5 m / ha x 100). Results of Tukey’s multiple comparison test showed significant differences in the Shannon-index (P = 0.04) between low (mean = 1.4) and moderate (mean = 1.7) edge densities (S8 Fig in S1 File). Differences in the Shannon-index were not sta- tistically significant between other ED categories. Nyssorhunchus darlingi was dominant in all 79 peridomestic habitats sampled. Variables analysis Results of Spearman‘s analysis showed that correlation among Ny. darlingi abundance and Shannon, Simpson and Berger-Parker indices were moderate and negative (Fig 4). Culicidae community richness showed a low, nonsignificant correlation with landscape components tested. Correlation between Ny. darlingi abundance and landscape A was low but significant (r = 0.2; P < 0.05). The PCA results showed three dimensions of components for Ny. darlingi abundance varia- tion, which explained 87.1% of the total variability observed (Dim.1 = 36.2%; Dim.2 = 28.9% and Dim.3 = 22.0%) (Table 2) in the data. The first component (Dim.1) showed a significant

negative correlation between the Shannon-index and Ny. darlingi abundance (P < 0.01). The second (Dim.2) and third (Dim.3) dimensions represented the landscape variables attributed to PLAND and ED. The Dim.2 dimension showed a positive correlation between both PLAND and ED variables (P < 0.01) and Ny. darlingi abundance (r = 0.24; P = 0.03), and PLOS ONE Anthropogenic landscape and mosquito biodiversity drives malaria vector proliferation PLOS ONE | https://doi.org/10.1371/journal.pone.0245087 January 14, 2021 8 / 25

Table 1. Species list of Culicidae collected in malaria endemic areas in Brazilian Amazon. PLAND (%) Edge density (m / ha) Peridomestic (HLC) Forest fringe (Shannon trap) Peridomestic (HLC) Forest fringe (Shannon trap) � 25 25 ¬ 45 45 ¬ 60 60 ¬ 75 > 75 � 25 25 ¬ 45 45 ¬ 60 60 ¬ 75 > 75 0 ¬ 1.5 1.5 ¬ 2.0 2.0 ¬ 3.5 0 ¬ 1.5 1.5 ¬ 2.0 2.0 ¬ 3.5 Species Author Taxonomic group n n/% n n/% n n/% n n/% n n/% n n/% n n/% n n/% n n/% n n/% n n/% n n/% n n/% n n/% n n/% n n/% Aedeomyia squamipennis (Lynch Arribalzaga) Aedeomyiini 3 0.1 0 0.0 6 0.2 1 0.0 2 0.1 136 4.1 60 2.6 81 4.1 4 0.2 9 0.5 2 0.1 3 0.1 7 0.2 135 4.6 62 1.7 93 1.7 Aedes albopictus (Skuse) Aedini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 Aedes argyrothorax Bonne-Wepster and Bonne Aedini 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 0 0.0 0 0.0 1 0.0 1 0.0 0 0.0 1 0.0 Aedes fulvithorax (Lutz) Aedini 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 Aedes fulvus (Wiedemann) Aedini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 45 1.3 11 0.5 5 0.3 20 0.8 58 3.4 0 0.0 0 0.0 0 0.0 53 1.8 57 1.6 29 0.5 Aedes fulvus sp1 Aedini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 7 0.2 0 0.0 0 0.0 3 0.1 0 0.0 0 0.0 0 0.0 0 0.0 7 0.2 0 0.0 3 0.1 Aedes hastatus Dyar Aedini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 Aedes hortator Dyar and Knab Aedini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 3 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 3 0.1

Aedes infirmatus Dyar and Knab Aedini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.0 Aedes scapularis (Rondani) Aedini 0 0.0 0 0.0 0 0.0 1 0.0 2 0.1 1 0.0 5 0.2 0 0.0 10 0.4 13 0.8 1 0.0 2 0.0 0 0.0 15 0.5 14 0.4 0 0.0 Aedes serratus (Theobald) Aedini 0 0.0 0 0.0 1 0.0 0 0.0 2 0.1 19 0.6 8 0.3 1 0.1 46 1.7 12 0.7 0 0.0 0 0.0 3 0.1 31 1.0 13 0.4 41 0.8 Aedes serratus / nubilus Aedini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 16 0.5 5 0.2 1 0.1 32 1.2 18 1.1 0 0.0 0 0.0 0 0.0 15 0.5 26 0.7 31 0.6 Psorophora albigenu (Peryassu ´) Aedini 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 2 0.1 2 0.1 7 0.4 21 0.8 10 0.6 0 0.0 1 0.0 0 0.0 14 0.5 15 0.4 13 0.2 Psorophora albipes (Theobald) Aedini 0 0.0 0 0.0 0 0.0 5 0.2 0 0.0 4 0.1 19 0.8 36 1.8 32 1.2 1 0.1 0 0.0 5 0.1 0 0.0 25 0.8 27 0.8 40 0.7 Psorophora amazonensis (Theobald) Aedini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 1 0.0 Psorophora cingulata (Fabricius) Aedini 1 0.0 6 0.2 23 0.9 19 0.8 21 1.2 65 1.9 42 1.8 173 8.7 47 1.8 32 1.9 3 0.1 26 0.4 45 1.0 61 2.1 42 1.2 248 4.5 Psorophora dimidiata Cerqueira Aedini 1 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 1 0.0 2 0.1 1 0.0 0 0.0 0 0.0 1 0.0 1 0.0 2 0.1 2 0.1 0 0.0 Psorophora discrucians (Walker) Aedini 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 Psorophora ferox (von Humboldt) Aedini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 4 0.1 1 0.0 2 0.1 4 0.2 3 0.2 0 0.0 0 0.0 0 0.0 3 0.1 4 0.1 7 0.1 Psorophora saeva Dyar and Knab Aedini 0 0.0 0 0.0 0

0.0 0 0.0 0 0.0 4 0.1 0 0.0 0 0.0 0 0.0 1 0.1 0 0.0 0 0.0 0 0.0 4 0.1 1 0.0 0 0.0 Anopheles costai da Fonseca & da Silva Ramos Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 4 0.2 0 0.0 18 0.7 17 1.0 0 0.0 0 0.0 0 0.0 4 0.1 3 0.1 32 0.6 Anopheles costai (near) Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 4 0.2 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 3 0.1 0 0.0 Anopheles costai G1 Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 0 0.0 5 0.3 2 0.1 6 0.4 0 0.0 0 0.0 2 0.1 1 0.0 5 0.1 7 0.1 Anopheles costai G2 Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 3 0.2 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 3 0.1 0 0.0 0 0.0 0 0.0 Anopheles costai G3 Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 11 0.5 1 0.1 0 0.0 5 0.3 0 0.0 0 0.0 0 0.0 7 0.2 10 0.3 0 0.0 Anopheles costai G4 Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 0 0.0 0 0.0 0 0.0 0 0.0 4 0.2 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 4 0.1 Anopheles costai G5 Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 Anopheles fluminensis (latu sensu ) Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 4 0.2 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 2 0.0 Anopheles fluminensis (strictu sensu ) Root Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 5 0.3 0 0.0 0 0.0 0 0.0 0 0.0 5 0.1 0 0.0 Anopheles fluminensis G1 Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 4 0.2 0 0.0 0 0.0 0 0.0 1 0.0 4 0.1 0 0.0 Anopheles fluminensis G2 Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 5 0.3 0 0.0 0 0.0 0 0.0 0 0.0 5 0.1 0 0.0

Anopheles fluminensis (near) Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 Anopheles forattini Wilkerson and Sallum Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 7 0.2 0 0.0 1 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 8 0.3 0 0.0 0 0.0 Anopheles malefactor (near) Dyar and Knab Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 Anopheles mattogrossensis Lutz and Neiva Anophelinae 0 0.0 0 0.0 0 0.0 5 0.2 0 0.0 0 0.0 1 0.0 0 0.0 2 0.1 0 0.0 0 0.0 5 0.1 0 0.0 2 0.1 0 0.0 1 0.0 Anopheles minor Da Costa Lima Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 7 0.4 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 8 0.3 1 0.0 0 0.0 Anopheles peryassui Dyar and Knab Anophelinae 0 0.0 1 0.0 0 0.0 1 0.0 0 0.0 0 0.0 1 0.0 20 1.0 25 1.0 20 1.2 0 0.0 0 0.0 2 0.1 0 0.0 32 0.9 34 0.6 Anopheles punctimacula Dyar and Knab Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 4 0.2 0 0.0 0 0.0 0 0.0 0 0.0 11 0.7 0 0.0 0 0.0 4 0.1 0 0.0 0 0.0 11 0.2 Anopheles punctimacula (near) Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 Anopheles rangeli Gabaldon, Cova Garcı ´a and Lopez Anophelinae 5 0.1 0 0.0 0 0.0 35 1.5 5 0.3 4 0.1 1 0.0 3 0.2 18 0.7 19 1.1 0 0.0 44 0.7 1 0.0 20 0.7 23 0.6 2 0.0 (Continued ) PLOS ONE Anthropogenic landscape and mosquito biodiversity drives malaria vector proliferation PLOS ONE | https://doi.org/10.1371/journal.pone.0245087 January 14, 2021 9 / 25

Table 1. (Continued) PLAND (%) Edge density (m / ha) Peridomestic (HLC) Forest fringe (Shannon trap) Peridomestic (HLC) Forest fringe (Shannon trap) � 25 25 ¬ 45 45 ¬ 60 60 ¬ 75 > 75 � 25 25 ¬ 45 45 ¬ 60 60 ¬ 75 > 75 0 ¬ 1.5 1.5 ¬ 2.0 2.0 ¬ 3.5 0 ¬ 1.5 1.5 ¬ 2.0 2.0 ¬ 3.5 Anopheles shannoni Davis Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 Anopheles sp . Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 1 0.0 Anopheles squamifemur Antunes Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 2 0.0 Chagasia fajardi (Lutz) Anophelinae 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 5 0.2 1 0.1 0 0.0 2 0.1 1 0.0 0 0.0 0 0.0 7 0.2 1 0.0 0 0.0 Chagasia sp . Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 3 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 3 0.1 Kerteszia neivai Howard, Dyar and Knab Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 2 0.1 0 0.0 0 0.0 0 0.0 2 0.1 2 0.1 0 0.0 Nyssorhynchus albitarsis Lynch Arriba ´lzaga Anophelinae 3 0.1 1 0.0 3 0.1 5 0.2 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 3 0.1 5 0.1 4 0.1 1 0.0 0 0.0 0 0.0 Nyssorhynchus albitarsis H Anophelinae 1 0.0 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 2 0.0 0 0.0 0 0.0 0 0.0 0 0.0 Nyssorhynchus arthuri C Anophelinae 0 0.0 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 12 0.5 0 0.0 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 12 0.2 Nyssorhynchus benarrochi Gabaldon, Cova- Garcı ´a and Lopez Anophelinae 6

0 0 0 0 0 1 0 0 0 0 0 6 0 1 0 0.0 Nyssorhynchus benarrochi B Anophelinae 0 0.0 2 0.1 0 0.0 0 0.0 1 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.0 1 0.0 0 0.0 0 0.0 0 0.0 Nyssorhynchus benarrochi G1 Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 3 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 3 0.1 0 0.0 0 0.0 Nyssorhynchus benarrochi G2 Anophelinae 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 1 0.1 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 1 0.0 1 0.0 0 0.0 Nyssorhynchus braziliensis (Chagas) Anophelinae 59 1.5 8 0.3 3 0.1 1 0.0 0 0.0 3 0.1 4 0.2 66 3.3 3 0.1 0 0.0 3 0.1 1 0.0 67 1.5 3 0.1 73 2.0 0 0.0 Nyssorhynchus darlingi Anophelinae 3473 86.9 2082 82.2 1839 71.0 1835 77.1 1291 70.8 395 11.8 285 12.2 179 9.0 60 2.3 371 21.9 2143 82.0 5493 87.0 2884 65.4 545 18.4 363 10.1 414 7.6 Nyssorhynchus deaneorum Rosa-Freitas Anophelinae 64 1.6 0 0.0 0 0.0 11 0.5 0 0.0 7 0.2 0 0.0 1 0.1 1 0.0 0 0.0 0 0.0 75 1.2 0 0.0 6 0.2 3 0.1 0 0.0 Nyssorhynchus dunhami Causey Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 1 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 1 0.0 Nyssorhynchus galvaoi Causey, Deane and Deane Anophelinae 0 0.0 0 0.0 0 0.0 10 0.4 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 10 0.4 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 Nyssorhynchus goeldii Rozeboom and Gabaldon Anophelinae 0 0.0 0 0.0 1 0.0 25 1.1 0 0.0 166 5.0 20 0.9 1 0.1 4 0.2 0 0.0 0 0.0 26 0.4 0 0.0 190 6.4 0 0.0 1 0.0 Nyssorhynchus konderi Galv ão & Damasceno Anophelinae 4 0.1 0 0.0 0 0.0 1 0.0 8 0.4 5 0.2 1 0.0 1 0.1 0 0.0 100 5.9 8 0.3 0 0.0 5 0.1 5 0.2 101 2.8 1 0.0

Nyssorhynchus konderi / oswaldoi Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.0 Nyssorhynchus konderi A Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 0 0.0 0 0.0 0 0.0 0 0.0 61 3.6 1 0.0 0 0.0 0 0.0 0 0.0 61 1.7 0 0.0 Nyssorhynchus konderi B Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 40 2.2 0 0.0 0 0.0 0 0.0 59 2.2 296 17.5 38 1.5 0 0.0 2 0.1 59 2.0 289 8.0 7 0.1 Nyssorhynchus nuneztovari Anophelinae 1 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 0 0.0 1 0.0 Nyssorhynchus nuneztovari A Anophelinae 1 0.0 0 0.0 0 0.0 12 0.5 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 12 0.2 1 0.0 0 0.0 0 0.0 0 0.0 Nyssorhynchus oryzalimnetes Wilkerson and Motoki Anophelinae 98 2.5 0 0.0 0 0.0 5 0.2 0 0.0 14 0.4 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 103 1.6 0 0.0 1 0.0 14 0.4 0 0.0 Nyssorhynchus oswaldoi (latu sensu ) Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 3 0.1 18 0.9 11 0.4 135 8.0 0 0.0 0 0.0 0 0.0 6 0.2 158 4.4 3 0.1 Nyssorhynchus oswaldoi (strictu sensu) (Peryassu ´) Anophelinae 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 Nyssorhynchus oswaldoi A Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 15 0.6 5 0.3 2 0.1 1 0.1 0 0.0 0 0.0 0 0.0 9 0.3 9 0.3 5 0.1 Nyssorhynchus sp . Anophelinae 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 Nyssorhynchus triannulatus (Neiva and Pinto) Anophelinae 0 0.0 27 1.1 7 0.3 22 0.9 3 0.2 136 4.1 210 9.0 132 6.7 174 6.6 21 1.2 42 1.6 14 0.2 3 0.1

391 13.2 103 2.9 179 3.3 Stethomyia thomasi Shannon Anophelinae 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 Culex (Aedinus ) amazonensis (Lutz) Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 30 1.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 30 0.6 Culex (Anoedioporpa ) originator Gordon and Evans Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 (Continued ) PLOS ONE Anthropogenic landscape and mosquito biodiversity drives malaria vector proliferation PLOS ONE | https://doi.org/10.1371/journal.pone.0245087 January 14, 2021 10 / 25

Table 1. (Continued) PLAND (%) Edge density (m / ha) Peridomestic (HLC) Forest fringe (Shannon trap) Peridomestic (HLC) Forest fringe (Shannon trap) � 25 25 ¬ 45 45 ¬ 60 60 ¬ 75 > 75 � 25 25 ¬ 45 45 ¬ 60 60 ¬ 75 > 75 0 ¬ 1.5 1.5 ¬ 2.0 2.0 ¬ 3.5 0 ¬ 1.5 1.5 ¬ 2.0 2.0 ¬ 3.5 Culex (Anoedioporpa ) sp. Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 1 0.0 1 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 1 0.0 Culex (Culex ) declarator Dyar and Knab Culicini 0 0.0 0 0.0 6 0.2 1 0.0 8 0.4 26 0.8 127 5.5 31 1.6 28 1.1 2 0.1 6 0.2 4 0.1 5 0.1 53 1.8 64 1.8 99 1.8 Culex (Culex ) Grupo Coronator Culicini 1 0.0 2 0.1 2 0.1 0 0.0 3 0.2 15 0.5 30 1.3 17 0.9 17 0.6 20 1.2 1 0.0 5 0.1 2 0.1 42 1.4 12 0.3 45 0.8 Culex (Culex ) mollis Dyar and Knab Culicini 1 0.0 0 0.0 5 0.2 2 0.1 9 0.5 1 0.0 252 10.8 8 0.4 7 0.3 36 2.1 7 0.3 4 0.1 6 0.1 72 2.4 214 5.9 17 0.3 Culex (Culex ) nigripalpus Theobald Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 2 0.1 4 0.2 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 1 0.0 5 0.1 Culex (Culex ) quinquefasciatus Say Culicini 171 4.3 153 6.0 98 3.8 126 5.3 15 0.8 0 0.0 0 0.0 5 0.3 0 0.0 10 0.6 98 3.8 36 0.6 429 9.7 1 0.0 7 0.2 11 0.2 Culex (Culex ) sp. Culicini 0 0.0 0 0.0 2 0.1 0 0.0 0 0.0 6 0.2 24 1.0 212 10.7 34 1.3 5 0.3 0 0.0 0 0.0 2 0.1 34 1.2 206 5.7 42 0.8 Culex (Melanoconion ) adamesi Sirivanakarn and Galindo Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 7 0.4 24 0.9 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 3 0.1 35 0.6 Culex (Melanoconion ) aureonotatus Duret and Barreto Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 5 0.2 0

0.0 3 0.1 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 7 0.1 Culex (Melanoconion ) bastagarius Dyar and Knab Culicini 19 0.5 14 0.6 4 0.2 1 0.0 134 7.4 100 3.0 99 4.3 54 2.7 130 4.9 130 7.7 117 4.5 33 0.5 22 0.5 162 5.5 164 4.6 187 3.4 Culex (Melanoconion ) clarki Evans Culicini 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 0 0.0 1 0.1 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 1 0.0 Culex (Melanoconion ) crybda Dyar Culicini 0 0.0 0 0.0 1 0.0 1 0.0 0 0.0 0 0.0 0 0.0 8 0.4 9 0.3 1 0.1 0 0.0 1 0.0 1 0.0 2 0.1 5 0.1 10 0.2 Culex (Melanoconion ) crybda sp1 Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 3 0.1 64 2.8 1 0.1 29 1.1 11 0.7 0 0.0 0 0.0 0 0.0 27 0.9 8 0.2 73 1.3 Culex (Melanoconion ) eknomios Forattini and Sallum Culicini 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 3 0.2 1 0.0 2 0.1 0 0.0 1 0.0 0 0.0 4 0.1 2 0.1 2 0.0 Culex (Melanoconion ) ensiformis Bonne-Wepster and Bonne Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 6 0.2 0 0.0 1 0.1 2 0.1 1 0.1 0 0.0 0 0.0 0 0.0 6 0.2 0 0.0 4 0.1 Culex (Melanoconion ) gnomatos Sallum, Hutchings, Leila and Ferreira Culicini 4 0.1 27 1.1 0 0.0 1 0.0 0 0.0 43 1.3 90 3.9 6 0.3 94 3.6 1 0.1 0 0.0 0 0.0 32 0.7 5 0.2 91 2.5 138 2.5 Culex (Melanoconion ) Grupo Atratus Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 19 0.6 11 0.5 27 1.4 1 0.0 6 0.4 0 0.0 0 0.0 0 0.0 32 1.1 31 0.9 1 0.0 Culex (Melanoconion ) Grupo Educator Culicini 0 0.0 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 7 0.3 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 7 0.2 0 0.0 Culex (Melanoconion ) Grupo Pilosus Culicini 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 0 0.0 2 0.1 1 0.1 5 0.2 3 0.2 0 0.0 1 0.0 0 0.0 5

0.2 2 0.1 4 0.1 Culex (Melanoconion ) inadmirabilis Dyar Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 24 1.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 24 0.7 0 0.0 Culex (Melanoconion ) ocossa Dyar and Knab Culicini 0 0.0 0 0.0 3 0.1 0 0.0 12 0.7 0 0.0 0 0.0 12 0.6 1 0.0 0 0.0 0 0.0 12 0.2 3 0.1 1 0.0 12 0.3 0 0.0 Culex (Melanoconion ) pedroi Sirivanakarn and Belkin Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 27 0.8 1 0.0 0 0.0 16 0.6 0 0.0 0 0.0 0 0.0 0 0.0 13 0.4 1 0.0 30 0.6 Culex (Melanoconion ) portesi Senevet and Abonnenc Culicini 3 0.1 48 1.9 1 0.0 2 0.1 11 0.6 335 10.0 352 15.1 40 2.0 175 6.6 6 0.4 1 0.0 2 0.0 62 1.4 0 0.0 392 10.9 516 9.4 Culex (Melanoconion ) ribeirensis Forattini and Sallum Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 6 0.2 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 5 0.1 1 0.0 Culex (Melanoconion ) sp. Culicini 1 0.0 14 0.6 3 0.1 0 0.0 2 0.1 26 0.8 146 6.3 44 2.2 163 6.2 50 3.0 6 0.2 3 0.1 11 0.3 51 1.7 126 3.5 252 4.6 Culex (Melanoconion ) sp. Sec ¸ão Melanoconion Culicini 5 0.1 10 0.4 0 0.0 1 0.0 0 0.0 0 0.0 2 0.1 3 0.2 6 0.2 0 0.0 0 0.0 2 0.0 14 0.3 2 0.1 3 0.1 6 0.1 Culex (Melanoconion ) spissipes (Theobald) Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 13 0.5 1 0.1 0 0.0 0 0.0 0 0.0 1 0.0 1 0.0 13 0.2 Culex (Melanoconion ) Sec¸ão Spissipes Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 2 0.1 0 0.0 Culex (Melanoconion ) theobaldi (Lutz) Culicini 10 0.3 31 1.2 6 0.2 30 1.3 63 3.5 2 0.1 1 0.0 13 0.7 15 0.6 30 1.8 7 0.3 81 1.3 52 1.2 22 0.7 18 0.5 21 0.4

Culex (Melanoconion ) vaxus Dyar Culicini 0 0.0 41 1.6 0 0.0 6 0.3 0 0.0 1 0.0 7 0.3 0 0.0 0 0.0 1 0.1 2 0.1 45 0.7 0 0.0 3 0.1 3 0.1 3 0.1 Culex (Melanoconion ) vomerifer Komp Culicini 0 0.0 6 0.2 0 0.0 0 0.0 0 0.0 11 0.3 17 0.7 18 0.9 28 1.1 2 0.1 0 0.0 0 0.0 6 0.1 0 0.0 13 0.4 63 1.2 (Continued ) PLOS ONE Anthropogenic landscape and mosquito biodiversity drives malaria vector proliferation PLOS ONE | https://doi.org/10.1371/journal.pone.0245087 January 14, 2021 11 / 25

Table 1. (Continued) PLAND (%) Edge density (m / ha) Peridomestic (HLC) Forest fringe (Shannon trap) Peridomestic (HLC) Forest fringe (Shannon trap) � 25 25 ¬ 45 45 ¬ 60 60 ¬ 75 > 75 � 25 25 ¬ 45 45 ¬ 60 60 ¬ 75 > 75 0 ¬ 1.5 1.5 ¬ 2.0 2.0 ¬ 3.5 0 ¬ 1.5 1.5 ¬ 2.0 2.0 ¬ 3.5 Culex (Melanoconion ) zeteki Dyar Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 22 0.7 6 0.3 2 0.1 3 0.1 0 0.0 0 0.0 0 0.0 0 0.0 25 0.8 5 0.1 3 0.1 Culex (Phe .) airozai Lane Culicini 0 0 0 0 2 0 4 1 0 3 0 0 2 0 4 4 0.1 Lutzia bigoti (Bellardi) Culicini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 Coquillettidia albicosta (Peryassu ´) Mansoniini 3 0.1 1 0.0 4 0.2 0 0.0 1 0.1 415 12.4 13 0.6 13 0.7 36 1.4 11 0.7 1 0.0 1 0.0 11 0.3 5 0.2 11 0.3 476 8.7 Coquillettidia arribalzagae (Theobald) Mansoniini 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 5 0.2 12 0.5 0 0.0 15 0.6 1 0.1 0 0.0 1 0.0 0 0.0 1 0.0 5 0.1 27 0.5 Coquillettidia chrysonotum (Peryassu ´) Mansoniini 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 14 0.4 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.0 0 0.0 14 0.5 0 0.0 0 0.0 Coquillettidia hermanoi (Lane and Coutinho) Mansoniini 1 0.0 2 0.1 0 0.0 0 0.0 0 0.0 49 1.5 0 0.0 0 0.0 3 0.1 18 1.1 2 0.1 1 0.0 0 0.0 27 0.9 6 0.2 37 0.7 Coquillettidia juxtamansonia (Chagas) Mansoniini 0 0.0 11 0.4 3 0.1 0 0.0 1 0.1 40 1.2 155 6.7 20 1.0 14 0.5 1 0.1 8 0.3 6 0.1 1 0.0 187 6.3 5 0.1 38 0.7 Coquillettidia lynchi (Shannon) Mansoniini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 15 0.6 0 0.0 9 0.3 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 8 0.2 14 0.3 Coquillettidia nigricans

(Coquillett) Mansoniini 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 2 0.1 0 0.0 14 0.7 1 0.0 0 0.0 0 0.0 0 0.0 2 0.1 2 0.1 0 0.0 15 0.3 Coquillettidia venezuelensis (Theobald) Mansoniini 19 0.5 4 0.2 6 0.2 5 0.2 3 0.2 1085 32.3 78 3.4 39 2.0 856 32.4 4 0.2 3 0.1 18 0.3 19 0.4 332 11.2 29 0.8 1713 31.3 Mansonia amazonensis (Theobald) Mansoniini 2 0.1 0 0.0 2 0.1 0 0.0 4 0.2 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 4 0.2 1 0.0 3 0.1 0 0.0 1 0.0 0 0.0 Mansonia flaveola (Coquillett) Mansoniini 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 Mansonia humeralis Dyar and Knab Mansoniini 0 0.0 2 0.1 359 13.9 14 0.6 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 2 0.1 2 0.1 4 0.1 370 8.4 0 0.0 0 0.0 7 0.1 Mansonia indubitans Dyar and Shannon Mansoniini 19 0.5 22 0.9 93 3.6 139 5.8 156 8.6 5 0.2 9 0.4 8 0.4 133 5.0 62 3.7 18 0.7 201 3.2 209 4.7 5 0.2 24 0.7 187 3.4 Mansonia pseudotitillans (Theobald) Mansoniini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 Mansonia sp . Mansoniini 0 0.0 0 0.0 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 0 0.0 0 0.0 Mansonia titillans (Walker) Mansoniini 14 0.4 7 0.3 105 4.1 51 2.1 7 0.4 42 1.3 6 0.3 572 28.9 68 2.6 1 0.1 66 2.5 26 0.4 92 2.1 155 5.2 530 14.7 3 0.1 Johnbelkinia longipes (Fabricius) Sabethini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 Limatus durhami Theobald Sabethini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 3 0.2 0 0.0 0 0.0 0 0.0 0

0.0 1 0.0 3 0.1 Trichoprosopon digitatum (Rondani) Sabethini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 2 0.1 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 5 0.1 Trichoprosopon sp . Sabethini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 1 0.0 0 0.0 1 0.0 3 0.2 0 0.0 0 0.0 0 0.0 1 0.0 1 0.0 4 0.1 Wyeomyia sp . Sabethini 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 5 0.3 0 0.0 0 0.0 1 0.0 2 0.1 1 0.0 3 0.1 Uranotaenia apicalis Theobald Uranotaeniini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 6 0.3 5 0.2 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 12 0.2 Uranotaenia calosomata Dyar and Knab Uranotaeniini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 3 0.1 1 0.1 3 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 6 0.1 Uranotaenia calosomata (near) Uranotaeniini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 Uranotaenia davisi Lane Uranotaeniini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 Uranotaenia geometrica Theobald Uranotaeniini 0 0.0 0 0.0 1 0.0 0 0.0 2 0.1 4 0.1 20 0.9 26 1.3 8 0.3 2 0.1 3 0.1 0 0.0 0 0.0 6 0.2 9 0.3 45 0.8 Uranotaenia lowii Theobald Uranotaeniini 0 0.0 0 0.0 0 0.0 0 0.0 1 0.1 0 0.0 5 0.2 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 5 0.1 Uranotaenia nataliae Lynch Arriba ´lzaga Uranotaeniini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 0 0.0 Uranotaenia pulcherrima Lynch Arriba ´lzaga Uranotaeniini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 5 0.3 6 0.2 1 0.1 0 0.0 0 0.0 0 0.0 0 0.0 1 0.0 13 0.2 Uranotaenia sp .

Uranotaeniini 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 2 0.0 Total 3996 100 2533 100 2592 100 2380 100 1823 100 3356 100 2330 100 1980 100 2642 100 1691 100 2612 100 6313 100 4410 100 2967 100 3603 100 5479 100 PLAND: forest cover percentage; HLC: human landing catch. https://doi.org/10.1371/journal.pone.0245087.t001 PLOS ONE Anthropogenic landscape and mosquito biodiversity drives malaria vector proliferation PLOS ONE | https://doi.org/10.1371/journal.pone.0245087 January 14, 2021 12 / 25

Dim.3, a positive correlation between ED and Ny. darlingi abundance (r = 0.29; P = 0.01), and negative for PLAND (S9 Fig in S1 File). The negative correlation in Dim.3 reinforced the rela- tionship trends between PLAND, and ED variables showed in S6 Fig in S1 File. In the forest fringe habitats, the difference in the Shannon index (mean = 1.7) calculated for fragmented forested landscape (C) and forested landscape (D) (mean = 1.2) was significant (P < 0.01). Differences between the Shannon index values calculated for fragmented open (B) and open (A) landscapes were not statistically significant (S10 Fig in S1 File). Considering the overall mosquito data from the peridomestic (HLC) plus forest fringe (ST) habitats, the results of Kruskal-Wallis test showed that the differences in Ny. darlingi abun- dance between fragmented forested (C), forested (D), fragmented open (B) and open (A) land- scapes were statistically significant (chi-squared = 9.9; P = 0.02). The Mann-Whitney- Wilcoxon test showed that difference in Ny. darlingi abundance found in the anthropogenic matrix (A) with low forest cover and low edge density, and the forested matrix (D) with high forest cover and low edge density, was statistically significant (P = 0.03). Nyssorhynchus dar- lingi showed higher abundance in anthropogenic matrix A (mean = 315 individuals) than in forest matrix D (mean = 62 individuals). Differences in the abundance of this species in frag- mented open B (mean = 85 individuals) and fragmented forest landscape C (mean = 133 indi-