Ecology

Spatial and temporal variations of the SRHII at daytime and nighttimeSignificant seasonal differences are observed in the SRHII in the YRDUA (Figure A1 and A2, Appendix A). In the daytime, RHI was concentrated in the Nanjing, “Su-Xi-Chang”, Ningbo, Shanghai, and Hangzhou metropolitan areas. Due to the high built-up areas and PD, the distribution of surface RHI is denser and stronger than that in the north and southwest of the YRDUA. The built-up area can absorb heat and store heat energy, which makes the surface temperatures rise rapidly. In spring and autumn, the spatial distribution of the RHI in spring or autumn was similar to that in summer except the spatial extent was tapered. However, the RHI gradually shrinks and transfers to the southern area of the YRDUA in winter, such as Linhai and Ningbo City, which is due to the relatively high solar radiation of the geographic location of the southern cities. The distance of the RHI is gradually shortened between cities and even into one piece from 2003 to 2017 due to long-term urban expansion and rapid growth of construction land (Figure A1, Appendix A). In the nighttime, the spatial pattern of the RHI is very different from that of the daytime. RHI mainly concentrates on Taihu Lake, Dianshan Lake, Ge Lake in the center part, Hongzhe Lake in the northwest, and Qiandao Lake in the southwest. Because water has a high specific heat capacity, it has the function of preserving heat at nighttime. Some cities like Shanghai, Hangzhou, and Nanjing have the strongest heat island in winter and the weakest heat island in summer. Urban areas usually have dense buildings, PD, and energy emissions, so there are more energy emissions at night. High surface albedo in urban areas at night leads to lower heat storage4,40 and ultimately resulting in smaller UHI at nighttime (Figure A2, Appendix A).From spring to summer and then summer to winter, RHI increases first and then decreases, and it reaches a peak in summer. For example, the proportion of the RHI was 12.65%, 31.03%, 21.12%, and 5.49% in spring, summer, autumn, and winter in 2017, respectively (Fig. 2d). An upward trend in the area of the RHI is observed from 2003 to 2017 in summer. In detail, the proportion of the heat island zone is 21.74%, 22.17%, and 31.03% in the summer of 2003, 2010, and 2017, respectively (Fig. 2d). It is because the urban areas of YRDUA have increased from 3571.01 km2 to 8760.26 km2 in 2003 and 2017, respectively (Figure B1, Appendix B). Moreover, the area of the medium heat island and strong heat island increased by 41.08% and 66.40% from 2003 to 2017 (Fig. 2b,c). A gradual decreasing trend is observed for the four grades of the SRHII (2–4 °C, 4–6 °C,  > 6 °C,  > 2 °C) in winter from 2003 to 2017 (Fig. 2a–d). The area of the RHI in winter was 18,481 km2, 8640 km2, and 6280 km2 in 2003, 2010, and 2017, respectively (Fig. 2d). Vegetation coverage is low in winter and bare soil is formed after harvest. It leads to the RHI decrease in winter. The above results indicated that the SRHII became increasingly hot in summer and increasingly cold in winter and that the trend became more obvious as the SRHII increased in the ranges of 2–4 °C, 4–6 °C,  > 6 °C. However, the seasonal variation of the RHI in the nighttime is opposite to that in the daytime. From spring to summer and then to winter, the area of the RHI decreases first and then increases, and it falls in the lowest value in summer (Fig. 2e–g). For example, the area of RHI is 19,209 km2, 5659 km2, 34,621 km2, and 38,596 km2 in spring, summer, autumn, and winter in 2017, respectively (Fig. 2h). The annual average of RHI regular increases, with values of 17,510 km2, 20,042 km2, and 20,097 km2 in 2003, 2010, and 2017, respectively (Fig. 2h).Figure 2Seasonal and inter-annual variations of the SRHII during the daytime (a–d) and nighttime (e–h) of the YRDUA.Full size imageRelationship between the SRHII and influencing factorsResults showed surface biophysical factors have a higher correlation with SRHII than socio-economic factors and climate factors in the day and night. NDBI and EVI have a stronger effect on SRHII than other biophysical factors in the day. NDBI showed a significant positive correlation with SRHII, while EVI showed a negative correlation with SRHII. In detail, NDBI (r = 0.567, p  autumn  > winter. The dominant influencing factor was the MNDWI in spring, autumn and winter, while EVI had the largest contribution in summer at night. More

Overview of the study areaThe experimental algae P. parvum was collected from the fishponds in Dawukou, Ningxia, China. Algal water samples were filtered by medium size filter paper and centrifuged, and cultured with F/2 culture medium in the following environmental conditions for 5 days; light intensity of 5000 lx, light/dark ratio of 12 h: 12 h, water temperature of 18.5 ± 0.5 ℃, pH of 8.5 ± 0.1 and salinity of 1.2 ± 0.1 mg/l20,21. The plate separation method was used to separate and purify the cultured algae22. After microscopy, the colony of pure algal cells were transferred to different volumes of triangular glass bottles which contains sterilized F/2 medium for expansion culture. Algae P. parvum propagates vegetatively by cell division, the cell density of algae increases exponentially during the process of propagation thus requires more space. To accommodate this increasing space requirement, different sizes of the triangular glass bottles were used as 50 ml, 250 ml and 10 l. The expansion cultures were maintained in the environmental conditions similar to the initial culture. The algal cells were used for the experiment when they reach the logarithmic growth stage (the logarithmic growth stage was reached in 10 days).Data collection and experimentationThe water sample from the 10 L expansion culture of P. parvum was collected. The initial nutrient concentrations and environmental factors were determined using appropriate methods and equipment in the laboratory. The initial nutrient concentrations and environmental conditions of the algae culture used in this experiment is presented in Table 1.Table 1 Initial nutrient concentrations and environmental conditions of algae culture used in the experiment.Full size tableExperimental factors and their levels for each nutrient concentrations and environmental factors were designed based on the above reference as shown in Table 2. We have designed eight levels for environmental factors (i.e., water temperature, pH and salinity) and ten levels for nutrient concentrations (i.e., nitrogen, phosphorous, silicon and iron).

1.

Evaluation of the effects of environmental factors on the growth of P. parvum

Table 2 Experimental factors and their levels designed for the experiment.Full size tableTo study the effects of environmental factors on the growth of P. parvum, water temperature, pH and salinity were used as the experimental factors by adopting the uniform design23,24,25 of three factors and eight levels as shown in Table 3.Table 3 Combination of environmental factors used for the different levels in the uniform design.Full size tableA 250 ml triangular glass bottle was used to implement each level of the above experiment with three replicates for each level (total of 24 bottles). The algae culture was allowed to grow in F/2 culture medium in the nutrient solution of 100 ml with an inoculation ratio of 1:10 (V/V). These bottles were kept in the light intensity of 5000 lx with light/dark ratio of 12 h: 12 h, while maintaining all other growth conditions to meet the experimental design requirements. The nutrient concentrations of N, P, Si and Fe were maintained at the level of initial concentrations (Table 1). Inoculated algae were cultured in a shaker for 10 days until it reaches its logarithmic growth stage and the growth rate was quantified.

2.

Evaluation of the effects of nutrient concentrations on the growth of P. parvum

To study the effects of nutrient concentrations on the growth of P. parvum, nitrogen, phosphorus, silicon and iron were used as experimental factors by adopting the uniform design5,26 of four factors and ten levels as shown in Table 4. The culture medium was prepared with sodium nitrate (NaNO3) as the nitrogen source, monosodium phosphate (NaH2PO4) as the phosphorus source, sodium metasilicate (Na2SiO3) as the silicon source, and ferric citrate (FeC6H5O7) as a source iron to obtain the appropriate concentrations of nitrogen, phosphorous, silicon and iron as designed for this experiment (Table 2).Table 4 Combination of nutrient concentration used for the different levels in the uniform design.Full size tableA 250 ml triangular glass bottle was used to implement each level of the above experiment with three replicates for each level (total of 30 bottles). The algae culture was allowed to grow in F/2 culture medium with a volume of 100 ml and an inoculation ratio of 1:10 (V/V). These bottles were kept in the light intensity of 5000 lx, light/dark ratio of 12 h: 12 h, water temperature of 18.5 ± 0.5 ℃, pH of 8.5 ± 0.1 and salinity of 1.2 ± 0.1 mg/l. Inoculated algae were cultured in a shaker for 10 days until it reaches its logarithmic growth stage and the biomass density was quantified.

3.

Determination of the growth rate of P. parvum

The algal cell density of the culture of each experimental level was measured using a 0.1 ml count plate under an optical microscope (Leica biological microscope DM1000, Leica Corporation, Oskar-Barnack-Straße, Germany) both at the beginning of the experiment and following 10 days of incubation period as the growth of the algae can reach its logarithmic growth stage at 10 days. Based on the algal cell density measurement, biomass density was calculated using the following formula (Eq. 1) described by Wei and Zhang;$$ Growth;rate;left( K right) = 3.322 times left( {log (N_{t} ) – log left( {N_{0} } right)} right)/left( {t – t_{0} } right) $$
(1)
where t is the duration of the experiment in days, N0 is the initial cell density (cell/ml) at the beginning of the experiment, and Nt is the cell density (cell/ml) at the end of day t of the experiment.Data analysis and results

1.

Establishment of the regression model between environmental factors and the growth rate

The growth rate of P. parvum under different levels of environmental factors are shown in Table 5, and the growth curve with time is shown in Fig. 1.Table 5 Growth rates of Prymnesium parvum under the different levels of environmental factors in the uniform design.Full size tableFigure 1The growth curve of P. parvum with time under different environmental factor levels.Full size imageIn multiple quadratic stepwise regression analysis, water temperature (X1), pH (X2) and salinity (X3) were taken as independent variables, and the growth rate (Y) was taken as the dependent variable. From this analysis a quadratic polynomial regression equation (Eq. 2) was developed as follows:$$ Y = – 11.0371 + 0.0682X_{1} + 2.5559X_{2} + 0.7953X_{3} – 0.0019X_{1} ^{2} – 0.1523{text{ }}X_{2} ^{2} – 0.3223{text{ }}X_{3} ^{2} $$
(2)
Correlation coefficient (R) of the above equation was 0.9994 and probability (P) of the regression equation was 0.025 (p  X3  > X1. Thus, the contribution of pH  > salinity  > water temperature on the growth rate of P. parvum.

2.

Evaluation of the effect of environmental factors on the growth rate of P. parvum

The environmental conditions that would result in the maximum growth rate of P. parvum were determined by optimizing the regression equation (Eq. 2). The following simple regression models (Eqs. 3–5) of multiple quadratic stepwise regression analyses reveal the relationships between individual environmental factors and the growth rate. These models were obtained by dimensionality reduction analysis in which the other factors were maintained at optimal levels.$$ X_{{1WT}} :;Yleft( {X_{1} } right) = 0.1768 + 0.0682X_{1} – 0.0019X_{1} ^{2} $$
(3)
$$ X_{{2pH}} :;Yleft( {X_{2} } right) = – 9.9345 + 2.5559X_{2} – 0.1523{text{ }}X_{2} ^{2} $$
(4)
$$ X_{{3salinity}} :;Yleft( {X_{3} } right) = 0.2982 + 0.7953X_{3} – 0.3223{text{ }}X_{3} ^{2} $$
(5)
The influence curves of each environmental factor on growth rate of P. parvum are shown in Fig. 2. The behavior of the curves is similar where the growth rate increases initially, then reaches a theoretical maximum and finally declines with increasing level of each environmental factor. Accordingly, P. parvum reaches its theoretical maximum growth rate (0.789) when the water temperature, pH and salinity is 18.11 ℃, 8.39 and 1.23‰, respectively. Therefore, Fig. 2 can be considered as the growth model of P. parvum as affected each of the respective environmental factors.

3.

Establishment of regression model between nutrient concentrations and the growth rate

Figure 2The growth rate of P. parvum as affected by the water temperature (a), pH (b) and salinity (c).Full size imageThe growth rates of P. parvum under the different levels of nutrient concentrations are shown in Table 7, and the growth curve with time is shown in Fig. 3.Table 7 Growth rate of Prymnesium parvum under the different levels of nutrient concentration in the uniform design.Full size tableFigure 3The growth curve of P. parvum with time under different nutrient concentrations factor levels.Full size imageA quadratic polynomial regression equation (Eq. 6) was generated using N (Xi), P (Xii), Si (Xiii) and Fe (Xiv) as independent variables and the growth rate (Y′) as the dependent variable by using multiple quadratic stepwise regression analysis as follows:$$ Y^{prime } = – 1.856686 + 1.371680X_{i} + 0.390361X_{{ii}} + 0.150656X_{{iii}} + 0.587990X_{{iv}} – {text{ }}0.2011178X_{i} ^{2} – 0.186640{text{ }}X_{{ii}} ^{2} – 0.108764{text{ }}X_{{iii}} ^{2} – 0.550523{text{ }}X_{{iv}} ^{2} $$
(6)
Correlation coefficient (R) of the above equation was 0.9994 and probability (P) of the regression equation was 0.035 ( Xii  > Xiv  > Xiii. Therefore, the contribution of nitrogen  > phosphorous  > iron  > silicon for the growth of P. parvum.

4.

Evaluation of the effect of nutrient concentrations on the growth rate of P. parvum

Multifactor square stepwise regression model was used to analyze the influence of individual nutrient concentration following the dimensionality reduction. To evaluate the influence of individual nutrient concentration on the growth rate, following sub-models (Eqs. 7–10) were developed by fixing other factors at the optimal level.$$ X_{i} nitrogen:Y^{prime } (X_{i} ) = – 1.4432 + 1.3717X_{i} – 0.2012X_{i} ^{2} $$
(7)
$$ X_{{ii}} phosphorus:Y^{prime } (X_{{ii}} ) = 0.6916 + 0.3904X_{{ii}} – 0.1866X_{{ii}} ^{2} $$
(8)
$$ X_{{iii}} silicon:Y^{prime } (X_{{iii}} ) = 0.8436 + 0.1507X_{{iii}} – 0.1088X_{{iii}} ^{2} $$
(9)
$$ X_{{iv}} iron:Y^{prime } (X_{{iv}} ) = 0.7388 + 0.5880X_{{iv}} – 0.5505X_{{iv}} ^{2} $$
(10)
The influence curves of each nutrients on growth rate of P. parvum are shown in Fig. 4. The behavior of the curves shows an initial increase of the growth rate, then the growth rate reaches a theoretical maximum and finally declines with increasing level of concentrations of each nutrient. Accordingly, P. parvum reaches its theoretical maximum growth rate (0.896) when the concentration of nitrogen, phosphorous, silicon and iron is 3.41, 1.05, 0.69, 0.53 mgl−1, respectively. Therefore, Fig. 4 may be considered as the growth model of P. parvum as affected each of the respective nutrients.Figure 4The growth rate of P. parvum as affected by nitrogen (a), phosphorus (b), silicon (c) and iron (d).Full size image More

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Ethics and vertebrate animalsThe field surveys and collections were conducted on accessible public areas or private residential areas with property owners’ permission. The study did not involve human participants, or endangered or protected species. Laboratory mice were used as a blood source for mosquitoes. All experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of the University of California, Irvine (UCI) (IACUC protocol number: AUP-19-165). All methods were carried out in accordance with relevant IACUC guidelines and regulations.Study sites and mosquito larval habitat surveillanceThe study was carried out in Orange County, California, USA. Orange County is a highly urbanized county with an estimated population density of approximately 1470 people/km2 according to U.S. Census Bureau, an average annual low/high temperature range of 13–25 °C, 65% relative humidity, and annual precipitation of about 350 mm according to U.S. Climate Data. Annual rainfall was 261 mm, 311 mm, 198 mm and 475 mm for 2016, 2017, 2018 and 2019, respectively. A major drought event occurred in December 2017 and February 2018 when the total rainfall in the 3-month period was 20.6% of the 30-year average. Both Ae. aegypti and Ae. albopictus were discovered in the county in 20158. Culex quinquefasciatus is the most abundant mosquito in the county and breeds readily in a variety of residential, commercial and USDS water sources, and is the primary vector of West Nile virus in southern California18.Larval mosquito surveillance in Orange County was conducted from 2016 to 2019 by the Orange County Mosquito and Vector Control District (OCMVCD) through its routine mosquito surveillance and treatment program, following the recommendations of the California Department of Public Health and the Mosquito and Vector Control Association of California19. Briefly, OCMVCD staff conducted routine inspection for aquatic habitats in randomly selected public areas, and performed door-to-door mosquito larval and adult sampling on residential or commercial premises upon the request of the residents or business owners while distributing public education materials for vector control and personal protection. Arial photography was used to examine the presence of abandoned swimming pools in residential areas. In addition to surface aquatic habitats, subsurface habitats (e.g., catch basins, underground drains, manhole chambers, and public utility vaults) were examined for larval abundance of all mosquito species. In 2019, OCMVCD completed 5,622 mosquito service requests, and conducted 11,813 inspection and treatments on routine sites using a variety of public health-approved adulticides and larvicides. A total of 38,099 underground drains and catch basins and 6925 km of flood channels were treated. In addition, a total of 17,783 km of gutters and 3562 neglected swimming pools were inspected and treated. The larval distribution data reported here were based on this extensive field sampling effort20.Larval sampling used standard mosquito dippers or pipettes, and specialized modifications of these to sample hard to reach areas. Mosquito larvae from each source were collected, transferred into a uniquely-numbered vial with isopropyl alcohol (70%), and submitted to the laboratory for identification; if present, live pupae were collected and held in site-specific labelled rearing chambers (BioQuip Products, Inc., Rancho Dominguez, CA) until emergence. Third and fourth instar mosquito larvae (1–100, depending on sample size) and emerged adults were identified to species using a stereo microscope (40–50x) and morphological features described in taxonomic keys21,22. Results were uploaded to OCMVCD’s data management system, along with collection date, GPS location, and habitat type for each sample site. For this study, larval habitats were classified into six types: small container, underground system, ornamental water features, marsh, pools/spas, and creek (Table S1). The container classification included flowerpots/vases, saucers, tires, bowls, boxes, buckets, dishes, tree holes, etc. Underground storm drain system referred to larval habitats such as catch basins, manhole chambers, underground drains, and public utility vaults that were below the ground. Water feature included flood control channels, ponds, fountains, birdbaths, street gutters and small reservoirs, etc. Marsh included both fresh and salt water marshes.Mosquito strains and water source for laboratory studiesWe examined the effect of USDS water on oviposition substrate preference and larval development in microcosms in an insectary with climate control (27 ± 1 °C, 70 ± 10% relative humidity, and 12 h light/12 h dark photoperiod) at UCI. To minimize potential bias on behavior and ecology from mosquito colonization, this study did not use previously established laboratory mosquito colonies. Instead, we used Ae. aegypti and Ae. albopictus adults reared from field-collected eggs using ovicups in residential areas of Orange and Los Angeles Counties, California, respectively. Culex quinquefasciatus were also reared from eggs of field-collected, blood-engorged adult mosquitoes using gravid traps in Orange County23.All experiments reported here used two types of habitat water: (1) USDS water collected from seven manhole chambers or catch basins (33°47′01.9″N, 117°53′19.0″W, Orange City, manhole; 33°52′25.0″N, 117°57′02.6″W, Fullerton City, manhole; 33°44′44.4″N, 118°06′24.2″W, Seal Beach City, manhole; 33°55′38.9″N, 117°56′51.4″W, La Habra City, manhole; 33°52′48.9″N, 117°55′21.4″W, Fullerton City, catch basin; 33°54′35.2″N, 117°56′02.5″W, Fullerton City, catch basin; 33°52′25.0″N, 117°57′02.6″W, Fullerton City, catch basin); and 2) flowerpot water from vases of three cemeteries in Orange County (33°50′29.0″N, 117°53′57.9″W; 33°46′21.5″N, 117°50′35.8″W; 33°46′12.3″N, 117°50′21.4″W). Water (including sediments) from each breeding source was collected with mosquito dippers and mixed together by habitat type into 18.9 L (five-gallon) Nalgene™ containers. The containers were transported to the laboratory in shaded ice containers, and stored overnight in a refrigerator at 4 °C. The experiments described below were conducted on the field-collected water for the two habitat types. We selected flowerpot water as the comparison substrate because flowerpot containers showed the highest larval positivity rate in the study area.Oviposition preference testTo examine whether USDS water attracts or repels egg laying by Ae. aegypti and Ae. albopictus mosquitoes, a two-choice oviposition preference test was conducted. Briefly, this experiment used two ovicups placed within a mosquito cage (1 × 0.5 × 0.5 m3), one ovicup with 200 ml USDS water and another with 200 ml flowerpot water. Adult mosquitoes were bloodfed on mice; fully engorged females 3-days post-bloodfeeding were used for oviposition preference tests. Ten gravid Ae. aegypti females were released into a cage and allowed to lay eggs for three days, and the number of eggs in each ovicup were counted. Five replicates were used. The same experiment was conducted for Ae. albopictus.To evaluate whether the presence of Cx. quinquefasciatus larvae has any impact on the egg laying behavior of invasive Aedes mosquitoes, the two-choice oviposition preference test described above was used. One ovicup contained 200 ml USDS water and ten first-instar Cx. quinquefasciatus larvae, while the second ovicup contained 200 ml USDS water only. Ten gravid Ae. aegypti or Ae. albopictus females were released into a cage and allowed to lay eggs for three days. Five replicates were used. We also conducted this experiment using flowerpot water with the same design and same number of replicates to determine whether the impact of Cx. quinquefasciatus larvae on Aedes mosquito egg laying behavior was similar across different water substrate types.Egg hatchingTo investigate the effects of different habitat water sources on egg hatching, 50 Ae. aegypti or Ae. albopictus eggs on separate filter papers were introduced into ovicups with 200 ml USDS water or flowerpot water. Deoxygenized distilled water that we routinely use in laboratory mosquito colony maintenance was used as a positive control. The experiment was conducted in an insectary with climate control (27 ± 1 °C). The number of larvae hatched were counted daily for six days continuously. Five replicates were used.Larval survivorshipA life table study was conducted on Ae. aegypti and Ae. albopictus larvae to determine the effect of USDS water and flowerpot water on larval development and survivorship. Twenty-five newly hatched Ae. aegypti or Ae. albopictus larvae were introduced into a microcosm that contained 200 ml USDS or field flowerpot water. The number of dead and surviving larvae was recorded daily until they pupated. Pupae were counted, and removed to different paper cups for emergence to adults. Four replicates were used for each type of habitat water per species. We included Cx. quinquefasciatus in the larval life table study for method validation purposes because the larvae of this species were known to successfully develop into pupae and adults in USDS water in southern California10.Larval survivorship experiments were conducted in two different seasons. The first was in the summer (August–September) 2019 when the density of invasive Aedes species peaked19, and also insecticide runoff from mosquito and residential/agricultural pest control applications were at the highest levels in southern California24. The second was in the winter (December) 2019 when there was little insecticide treatment for mosquito and pest control. This design enabled us to examine seasonality in larval survivorship and the impact of environmental insecticide runoff in USDS water. To determine whether USDS water’s nutritional deficiency plays a major role in limiting Aedes larval development, we repeated the larval survival experiment by adding 0.1 g Tetramin Tropical Flakes, the standard larval mosquito diet in insectaries, to the microcosms every 2 days. The number of dead and surviving larvae, pupae, and emergent adults was recorded daily.Data analysisAll aquatic habitats that were positive or negative for the larvae of Ae. aegypti, Ae. albopictus and Cx. quinquefasciatus (the predominant species), were mapped using ArcGIS 10.7.1. The proportion of aquatic habitats positive for Ae. aegypti and Cx. quinquefasciatus was calculated for each habitat type from 2016 to 2019. To examine variation in Aedes and Culex larval positivity rate among different groups of larval habitats within the USDS, larval positivity rates for Ae. aegypti and Cx. quinquefasciatus were calculated for underground water retention vaults, underground catch basins/manholes, and underground pipelines/tunnels. The Chi-square test was used to examine the statistical significance. Culex quinquefasciatus was analyzed because it was the most common species, whereas Ae. albopictus was not included in the analysis due to insufficient number of Ae. albopictus positive habitats. To determine whether USDS water attracted or repelled oviposition of invasive Aedes mosquitoes, a pairwise t test was used to compare egg number in USDS water ovicups to flowerpot water ovicups for each Aedes species. 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Forest cover influences water quality variables of ponds by shading, organic matter inputs and erosion processes43. These effects have consequences for pond water quality44 and favor the establishment of different culicid species45. We showed that during the rainy season, forest cover directly and indirectly influenced site water quality. Greater forest cover in the rainy season directly and indirectly affected A. nimbus and the secondary malaria vectors A. triannulatus and A. braziliensis positively. In the dry season, greater forest cover positively but marginally affected A. peryassui, A. nuneztovari and A. albitarsis, but only indirectly through water quality. Some species like A. triannulatus, A. nuneztovari and A. braziliensis coexist with the malaria vector, A. darlingi, in breeding sites46, and these species have been positively associated with pH, dissolved oxygen and total suspended solids in natural and artificial habitats20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47, which are environmental conditions favored by greater forest cover. The marginal indirect effect of forest cover on anopheline assemblage in the dry season suggests that we need caution in the interpretation of this result and long-term temporal data is required to confirm if this effect is corroborated.Forest conditions influence mosquito vectors and their hosts. For example, some mosquitoes are zoophiles that feed on the blood of birds, reptiles, and mammals48, which are often more abundant in conserved areas. Other species are anthropophilic and prefer to feed on human blood49 and altered environments can force these species to migrate and, consequently, to change hosts48. In our study, A. triannulatus and A. minbus were more abundant in sites with more natural characteristics, whereas A. darlingi and A. nuneztovari were more abundant in altered landscapes. In addition, urbanization and deforestation increase the proximities of humans and domestic animals to mosquito vectors and their hosts, thereby maintaining and increasing transmission cycles50.Forest conditions influence anopheline diversity by different paths, which may alter the strength of their seasonal effects. During the dry season, mosquito survival is also affected by altered microclimate (e.g., lower humidity)51 and lentic habitats contain less water, increased nutrient concentrations and decreased abundance and richness of mosquitoes52,53. We observed that rainfall plays an important role in the larval abundance of Anopheles in artificial larval habitats in Manaus. In addition, climatic factors such as rainfall and river levels are strongly associated with vector abundance and malaria cases in the region54,55. During the rainy season, increased water volume in artificial habitats provides more areas for distribution and development of mosquito species56 and we detected a significant increase in abundance of A. triannulatus, A. darlingi and A. nuneztovari. These observations may partially explain why we found a direct effect of forest cover on mosquitoes only during the rainy season.Our results add more evidence that managing and conserving forest cover is important to control anophelines, thereby decreasing the contact of potential vectors (e.g., A. darlingi) with humans. In general, our results support the idea that mosquitoes are directly affected by the loss of native forest cover57 in the rainy season. Mosquitoes associated with serious human diseases (e.g., malaria, yellow fever, dengue, leishmaniasis) are more abundant in areas with low levels of native forest cover14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58. This is a critically important finding because recent studies have shown that forest cover plays an important role in the vector dynamics of mosquitoes and forest conservation keeps pathogens within the forest, avoiding spillover to human settlements59. On the other hand, deforestation provides favorable conditions for these vectors, thereby increasing malária cases and decreasing scores of the Human Development Index60. In addition, there is a positive correlation between mosquito abundance in fragmented forests and the prevalence of Plasmodium, the protozoan that causes malaria61.Artificial larval habitats promote conditions for malaria vectors in Amazônia62,63. Therefore, the best way to develop control techniques would be to understand larval ecology in these habitats, where they are more sensitive to infections by pathogens, parasites, predation, larvicides and growth regulators64. This information is necessary to minimize failures in programs to control or eradicate the vector and the disease. Under this perspective, our study adds a new piece in the puzzle of mosquito control in Amazônia. For example, during the rainy season when forest cover directly and indirectly influences larval habitats, control programs can strengthen the control of key limnological variables, habitat structure, and entomological aspects, intensifying the environmental filter, particularly in areas with little forest cover and greater human concentrations near those habitats. The limnological study of Anopheles larval habitats is still far from complete, as each case has peculiarities inherent to them. Despite attempts, anophelines demonstrate versatility in relation to abiotic parameters20,21,22,23,24,25,26,65,66. However, we can use approaches that modify the larval environments. For example, more efficient management of water levels in fish farming ponds could decrease larval numbers and anopheline reproduction, Similarly, greater rationing of fish feed would decrease the supply of food resources for mosquito larvae. It is also worth mentioning that some variables are related to the efficiency of others. Regarding biological control via entomopathogenic bacteria, environmental factors (solar radiation) and water quality (amounts of total suspended solids and organic matter), can interfere with the effectiveness of the formulated Bacillus sphaericus applied in habitats for vector control62,63,64,65,66,67. Furthermore, eutrophication decreased the assemblages of aquatic invertebrates predating mosquito larvae.Another alternative is the use of physical control (removal of grasses and macrophytes from the edge of habitats), helping to reduce microhabitats that provide larval refuges. Also, increased light and water temperature at the edges favor natural predation and biological control processes from potential fish and macroinvertebrates. The conservation of natural enemies and the use of biotic agents in the population control of vector mosquitoes have been recommended in small and medium-sized natural and artificial breeding sites19,20,21,22,23,24,25,26,27,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53. A combination of techniques that shape the important environmental variables for the establishment of these species are essential for vector control.The analytical approach used here opens some windows of opportunity for improvements that are important to be recognized. First, our model did not incorporate important complexity of natural systems, such as ecological interactions among vectors and hosts, including human behavior. Agent-based models, including different host behavior, could provide important insights in this way. Second, our study is very limited in terms of temporal climatic variability. Additional information is needed to better understand the effects of long-term changes in land-use, water quality and climate and their interactions with mosquito assemblages in the region, particularly considering an ecological-evolutionary perspective. Third, it is important to highlight that the magnitude of effects of the estimated drivers were not the same in the rainy and dry seasons. Also, they may not remain constant in coming decades, especially considering potential regional process on mosquito assemblages, such as spillover effects, mass effects and host changes. Fourth, our study was carried out in an area of Amazonia that has experienced, a relatively old land use conversion from forest to urban areas (urban expansion rate of around 12% per year for the past 34 years)68. Beginning in the 1970s, human population increased at a rate of around 23% per decade and 25% in Manaus11. Therefore, the region we studied is very relevant in terms of historical interactions among human populations, mosquitoes and land use changes. However, understanding the effect of these changes on mosquito assemblages in areas with different land-use change dynamics, provides us with important information69, particularly those with very rapid urbanization processes, such as in the Arch of Deforestation70. Lastly, we need studies that consider the nexus among climate and land use changes, human and animal population health, economic conditions, and ecosystem services provided by these forest-urban transitional regions. Such information would facilitate including mosquito information in land use planning and climate mitigation programs based on forest management in and around cities.Therefore, identifying ecological factors and paths that affect the composition of species of epidemiological importance are essential because they inform vector integrated management strategies. We emphasize that larval control in lentic habitats requires knowledge about larval ecology and the effects of biotic and abiotic variables on larvae, especially when it comes to biological controls. The application of integrated pest management can be conducted in both dry and rainy seasons. However, we recommend focusing on the dry season when larval habitats are more limited, in smaller volumes and more accessible for entry and application of vector control techniques. These are critically important considerations because over 2 million people live in Amazonas state11 and anophelines transmitted over 59,637 malaria cases in the Amazon region in the first half of 2020, and about 44.4% came from the state of Amazonas71. More