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

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.