Ecology

1.Morris, J. L. et al. The timescale of early land plant evolution. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1719588115 (2018).Article 
PubMed 

Google Scholar 
2.Gibling, M. R. & Davies, N. S. Palaeozoic landscapes shaped by plant evolution. Nat. Geosci. 5, 99–105 (2012).ADS 
CAS 
Article 

Google Scholar 
3.Gibling, M. R. et al. Palaeozoic co-evolution of rivers and vegetation: A synthesis of current knowledge. Proc. Geol. Assoc. 125, 524–533 (2014).Article 

Google Scholar 
4.Mitchell, R. L. et al. Mineral weathering and soil development in the earliest land plant ecosystems. Geology 44, 1007–1010 (2016).ADS 
CAS 
Article 

Google Scholar 
5.Mergelov, N. et al. Alteration of rocks by endolithic organisms is one of the pathways for the beginning of soils on Earth. Sci. Rep. 8, 1–15 (2018).CAS 
Article 

Google Scholar 
6.McMahon, W. J. & Davies, N. S. Evolution of alluvial mudrock forced by early land plants. Science 359, 1022–1024 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Field, K. J. et al. Functional analysis of liverworts in dual symbiosis with Glomeromycota and Mucoromycotina fungi under a simulated Palaeozoic CO2 decline. ISME J. 10, 1514–1526 (2016).CAS 
PubMed 
Article 

Google Scholar 
8.Mills, B., Watson, A. J., Goldblatt, C., Boyle, R. & Lenton, T. M. Timing of Neoproterozoic glaciations linked to transport-limited global weathering. Nat. Geosci. 4, 861–864 (2011).ADS 
CAS 
Article 

Google Scholar 
9.Porada, P., Weber, B., Elbert, W., Pöschl, U. & Kleidon, A. Estimating impacts of lichens and bryophytes on global biogeochemical cycles. Global Biogeochem. Cycles 28, 71–85 (2014).ADS 
CAS 
Article 

Google Scholar 
10.Edwards, D., Cherns, L. & Raven, J. A. Could land-based early photosynthesizing ecosystems have bioengineered the planet in mid-Palaeozoic times?. Palaeontology 58, 803–837 (2015).Article 

Google Scholar 
11.Williams, A. J., Buck, B. J. & Beyene, M. A. Biological soil crusts in the mojave desert, USA: micromorphology and pedogenesis. Soil Sci. Soc. Am. J. 76, 1685 (2012).ADS 
CAS 
Article 

Google Scholar 
12.Belnap, J. & Lange, O. L. Biological Soil Crusts: Structure, Function, and Management (Springer, 2001).
Google Scholar 
13.Mitchell, R. L. et al. Cryptogamic ground covers as analogues for early terrestrial biospheres: Initiation and evolution of biologically mediated soils. Geobiology 00, 1–15 (2021).
Google Scholar 
14.Kenrick, P., Wellman, C. H., Schneider, H. & Edgecombe, G. D. A timeline for terrestrialization: Consequences for the carbon cycle in the Palaeozoic. Philos. Trans. R. Soc. B Biol. Sci. 367, 519–536 (2012).Article 

Google Scholar 
15.Strullu-Derrien, C., Wawrzyniak, Z., Goral, T. & Kenrick, P. Fungal colonization of the rooting system of the early land plant Asteroxylon mackiei from the 407-Myr-old Rhynie Chert (Scotland, UK). Bot. J. Linn. Soc. 179, 201–213 (2015).Article 

Google Scholar 
16.Krings, M., Kerp, H., Hass, H., Taylor, T. N. & Dotzler, N. A filamentous cyanobacterium showing structured colonial growth from the Early Devonian Rhynie chert. Rev. Palaeobot. Palynol. 146, 265–276 (2007).Article 

Google Scholar 
17.Remy, W., Taylort, T. N., Hass, H. & Kerp, H. Four Hundred-million-year-old Vesicular Arbuscular Mycorrhizae (Endomycorrhiae/symbiosis/fossil fungi/mutualims). Proc. Natl. Acad. Sci. United States Am. 91, 11841–11843 (1994).ADS 
CAS 
Article 

Google Scholar 
18.Field, K. J. et al. Contrasting arbuscular mycorrhizal responses of vascular and non-vascular plants to a simulated Palaeozoic CO2 decline. Nat. Commun. 3, 1–8 (2012).Article 
CAS 

Google Scholar 
19.Lenton, T. M., Crouch, M., Johnson, M., Pires, N. & Dolan, L. First plants cooled the Ordovician. Nat. Geosci. 5, 86–89 (2012).ADS 
CAS 
Article 

Google Scholar 
20.Mills, B. J. W., Batterman, S. A. & Field, K. J. Nutrient acquisition by symbiotic fungi governs Palaeozoic climate transition. Phil. Trans. R. Soc. B 373, 20160503 (2017).21.Mitchell, R. L., Strullu-Derrien, C. & Kenrick, P. Biologically mediated weathering in modern cryptogamic ground covers and the early paleozoic fossil record. J. Geol. Soc. London. 176, 430–439 (2019).CAS 
Article 

Google Scholar 
22.Furnes, H. et al. Comparing petrographic signatures of bioalteration in recent to Mesoarchean pillow lavas: Tracing subsurface life in oceanic igneous rocks. Precambrian Res. 158, 156–176 (2007).ADS 
CAS 
Article 

Google Scholar 
23.Smits, M. M. et al. Plant-driven fungal weathering: Early stages of mineral alteration at the nanometer scale. Geology 37, 615–618 (2009).ADS 
Article 
CAS 

Google Scholar 
24.Bonneville, S. et al. Tree-mycorrhiza symbiosis accelerate mineral weathering: Evidences from nanometer-scale elemental fluxes at the hypha-mineral interface. Geochim. Cosmochim. Acta 75, 6988–7005 (2011).ADS 
CAS 
Article 

Google Scholar 
25.McLoughlin, N. Fungal origins?. Nat. Ecol. Evol. 1, 1–2 (2017).Article 

Google Scholar 
26.Ivarsson, M. et al. Intricate tunnels in garnets from soils and river sediments in Thailand-Possible endolithic microborings. PLoS ONE 13, 0200351 (2018).Article 
CAS 

Google Scholar 
27.Hoffland, E. et al. The role of fungi in weathering. Front. Ecol. Environ. 2, 258–264 (2004).Article 

Google Scholar 
28.McLoughlin, N., Furnes, H., Banerjee, N. R., Muehlenbachs, K. & Staudigel, H. Ichnotaxonomy of microbial trace fossils in volcanic glass. J. Geol. Soc. London. 166, 159–169 (2009).Article 

Google Scholar 
29.Berner, R. A. & Cochran, M. F. Plant-induced weathering of Hawaiian basalts. J. Sediment. Res. 68, 723–726 (1998).ADS 
CAS 
Article 

Google Scholar 
30.Landeweert, R., Hoffland, E., Finlay, R. D., Kuyper, T. W. & Van Breemen, N. Linking plants to rocks: Ectomycorrhizal fungi mobilize nutrients from minerals. Trends Ecol. Evol. 16, 248–254 (2001).CAS 
PubMed 
Article 

Google Scholar 
31.Van Schöll, L. et al. Rock-eating mycorrhizas: Their role in plant nutrition and biogeochemical cycles. Plant Soil 303, 35–47 (2008).Article 
CAS 

Google Scholar 
32.Quirk, J. et al. Evolution of trees and mycorrhizal fungi intensifies silicate mineral weathering. Biol. Lett. 8, 1006–1011 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Daly, M. et al. A multi-scale correlative investigation of ductile fracture. Acta Mater. 130, 56–68 (2017).ADS 
CAS 
Article 

Google Scholar 
34.Gelb, J., Finegan, D. P., Brett, D. J. L. & Shearing, P. R. Multi-scale 3D investigations of a commercial 18650 Li-ion battery with correlative electron- and X-ray microscopy. J. Power Sour. 357, 77–86 (2017).ADS 
CAS 
Article 

Google Scholar 
35.Slater, T. J. A. et al. Multiscale correlative tomography: An investigation of creep cavitation in 316 stainless steel. Sci. Rep. 7, 1–10 (2017).CAS 
Article 

Google Scholar 
36.Burnett, T. L. & Withers, P. J. Completing the picture through correlative characterization. Nat. Mater. 18, 1041–1049 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
37.Mitchell, R. L. et al. Macro-to-nanoscale investigation of wall-plate joints in the acorn barnacle Semibalanus balanoides: correlative imaging, biological form and function, and bioinspiration. J. R. Soc. Interface 16, 20190218 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Bradley, R. S. & Withers, P. J. Correlative multiscale tomography of biological materials. MRS Bull. 41, 549–556 (2016).ADS 
Article 

Google Scholar 
39.Ferstl, S. et al. Nanoscopic X-ray tomography for correlative microscopy of a small meiofaunal sea-cucumber. Sci. Rep. 10, 1–12 (2020).Article 
CAS 

Google Scholar 
40.O’Sullivan, J. D. B., Cruickshank, S. M., Starborg, T., Withers, P. J. & Else, K. J. Characterisation of cuticular inflation development and ultrastructure in Trichuris muris using correlative X-ray computed tomography and electron microscopy. Sci. Rep. 10, 1–9 (2020).Article 
CAS 

Google Scholar 
41.Goral, J., Walton, I., Andrew, M. & Deo, M. Pore system characterization of organic-rich shales using nanoscale- resolution 3D imaging. Fuel 258, 116049 (2019).CAS 
Article 

Google Scholar 
42.Andrew, M. Comparing organic-hosted and intergranular pore networks: topography and topology in grains, gaps and bubbles. Geol. Soc. Lond. Spec. Publ. 484, 4844 (2018).
Google Scholar 
43.Ma, L. et al. Correlative multi-scale imaging of shales: a review and future perspectives. Geol. Soc. Lond. Spec. Publ. 454, 175–199 (2017).ADS 
Article 

Google Scholar 
44.Schlüter, S., Eickhorst, T. & Mueller, C. W. Correlative imaging reveals holistic view of soil microenvironments. Environ. Sci. Technol. 53, 829–837 (2019).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
45.Bandara, C. D. et al. High-Resolution Chemical Mapping and Microbial Identification of Rhizosphere using Correlative Microscopy. bioRxiv 1–26 (2021).46.Spruzeniece, L., Piazolo, S., Daczko, N. R., Kilburn, M. R. & Putnis, A. Symplectite formation in the presence of a reactive fluid: insights from hydrothermal experiments. J. Metamorph. Geol. 35, 281–299 (2017).ADS 
CAS 
Article 

Google Scholar 
47.Stefánsson, A. et al. Major impact of volcanic gases on the chemical composition of precipitation in Iceland during the 2014–2015 Holuhraun eruption. J. Geophys. Res. Atmos. Geophys. Res. Atmos. 122, 1971–1982 (2017).ADS 
Article 
CAS 

Google Scholar 
48.Gadd, G. M. Metals, minerals and microbes: Geomicrobiology and bioremediation. Microbiology 156, 609–643 (2010).CAS 
PubMed 
Article 

Google Scholar 
49.Jongmans, A. G. et al. Rock-eating fungi. 389, 682–683 (1997).CAS 

Google Scholar 
50.Gadd, G. M. Fungi, rocks, and minerals. Elements 13, 171–176 (2017).Article 

Google Scholar 
51.Warscheid, T. & Braams, J. Biodeterioration of stone: a review. Int. Biodeterior. Biodegredation 46, 343–368 (2000).CAS 
Article 

Google Scholar 
52.Burghelea, C. et al. Mineral nutrient mobilization by plants from rock: influence of rock type and arbuscular mycorrhiza. Biogeochemistry 124, 187–203 (2015).CAS 
Article 

Google Scholar 
53.Mcloughlin, N., Staudigel, H., Furnes, H., Eickmann, B. & Ivarsson, M. Mechanisms of microtunneling in rock substrates: Distinguishing endolithic biosignatures from abiotic microtunnels. Geobiology 8, 245–255 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Hoffland, E., Giesler, R., Jongmans, T. & Van Breemen, N. Increasing feldspar tunneling by fungi across a North Sweden podzol chronosequence. Ecosystems 5, 11–22 (2002).Article 

Google Scholar 
55.Wierzchos, J. & delos Ríos A, Ascaso C, ,. Microorganisms in desert rocks: The edge of life on Earth. Int. Microbiol. 15, 173–183 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Ascaso, C. & Wierzchos, J. New approaches to the study of Antarctic lithobiontic microorganisms and their inorganic traces, and their application in the detection of life in Martian rocks. Int. Microbiol. 5, 215–222 (2003).
Google Scholar 
57.Gorbushina, A. A., Boettcher, M., Brumsack, H. J., Krumbein, W. E. & Vendrell-Saz, M. Biogenic forsterite and opal as a product of biodeterioration and lichen stromatolite formation in table mountain systems (Tepuis) of Venezuela. Geomicrobiol. J. 18, 117–132 (2001).CAS 
Article 

Google Scholar 
58.Adamo, P. & Violante, P. Weathering of rocks and neogenesis of minerals associated with lichen activity. Appl. Clay Sci. 16, 229–256 (2000).CAS 
Article 

Google Scholar 
59.Oggerin, M., Tornos, F., Rodriguez, N., Pascual, L. & Amils, R. Fungal iron biomineralization in Río Tinto. Minerals 6, 37 (2016).Article 
CAS 

Google Scholar 
60.Akhtar, M. E. & Kelso, W. I. Electron microscopic characterisation of iron and manganese oxide/hydroxide precipitates from agricultural field drains 1. Biol. Fertil. Soils 16, 305–312 (1993).CAS 
Article 

Google Scholar 
61.Gadd, G. M. Fungal production of citric and oxalic acid: importance in metal speciation, physiology and biogeochemical processes. Adv. Microb. Physiol. 41, 47–92 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Napieralski, S. A. et al. Microbial chemolithotrophy mediates oxidative weathering of granitic bedrock. Proc. Natl. Acad. Sci. U. S. A. 116, 26394–26401 (2019).ADS 
CAS 
PubMed Central 
Article 

Google Scholar 
63.Dorn, R. I., Mahaney, W. C. & Krinsley, D. H. Case hardening: turning weathering rinds into protective shells. Elements 13, 165–169 (2017).Article 

Google Scholar 
64.Schreiber, H. D. Experimental studies of nickel and chromium partitioning into olivine from synthetic basaltic melts. in Lunar and Planetary Science Conference, 10th, Houston, Texas, Proceedings Volume 1 509–516 (1979).65.Burford, E. P., Kierans, M. & Gadd, G. M. Geomycology: Fungi in mineral substrata. Mycologist 17, 98–107 (2003).Article 

Google Scholar 
66.Dorn, R. I., Gordon, S. J., Krinsley, D. & Langworthy, K. Nanoscale: Mineral Weathering Boundary. In: Treatise on Geomorphology (eds. Shroder, J., Pope, G. A.), vol. 4, 44–69 (2013).67.Smits, M. Mineral tunneling by fungi. in Fungi in Biogeochemical cycles (ed. Gadd, G. M.) 311–327 (Cambridge University Press, 2006).68.Gorbushina, A. A. Life on the rocks. Environ. Microbiol. 9, 1613–1631 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Gadd, G. M. Geomycology: biogeochemical transformations of rocks, minerals, metals and radionuclides by fungi, bioweathering and bioremediation. Mycol. Res. 111, 3–49 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Arocena, J. M., Zhu, L. P. & Hall, K. Mineral accumulations induced by biological activity on granitic rocks in Qinghai Plateau China. Earth Surf. Process. Landforms 28, 1429–1437 (2003).ADS 
CAS 
Article 

Google Scholar 
71.Krumbein, W. E. & Jens, K. Biogenic rock varnishes of the Negev desert (Israel) an ecological study of iron and manganese transformation by cyanobacteria and fungi. Oecologia 50, 25–38 (1981).ADS 
CAS 
PubMed 
Article 

Google Scholar 
72.Gadd, G. M. Microbial formation and transformation of organometallic and organometalloid compounds. FEMS Microbiol. Rev. 11, 297–316 (1993).CAS 
Article 

Google Scholar 
73.Mitchell, R. L. et al. What lies beneath: 3d vs 2d correlative imaging challenges and how to overcome them. Microsc. Microanal. 25, 416–417 (2019).Article 

Google Scholar 
74.Thévenaz, P., Ruttimann, U. E. & Unser, M. A pyramid approach to subpixel registration based on intensity. IEEE Trans. Image Process. 7, 27–41 (1998).ADS 
PubMed 
Article 

Google Scholar  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

1.Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, 2478 (2015).Article 
CAS 

Google Scholar 
2.Owen-Smith, N., Fryxell, J. M. & Merrill, E. H. Foraging theory upscaled: The behavioural ecology of herbivore movement. Philos. Trans. R. Soc. B Biol. Sci. 365, 2267–2278 (2010).CAS 
Article 

Google Scholar 
3.Spiegel, O., Leu, S. T., Bull, C. M. & Sih, A. What’s your move? Movement as a link between personality and spatial dynamics in animal populations. Ecol. Lett. 20, 3–18 (2017).PubMed 
Article 
ADS 

Google Scholar 
4.Morales, J. M. et al. Building the bridge between animal movement and population dynamics. Philos. Trans. R. Soc. B Biol. Sci. 365, 2289–2301 (2010).Article 

Google Scholar 
5.Earl, J. E. & Zollner, P. A. Advancing research on animal-transported subsidies by integrating animal movement and ecosystem modelling. J. Anim. Ecol. 86, 987–997 (2017).PubMed 
Article 

Google Scholar 
6.Bastille-Rousseau, G. & Wittemyer, G. Unveiling multidimensional heterogeneity in resource selection and movement tactics of animal. Ecol. Lett. https://doi.org/10.1111/ele.13327 (2019).Article 
PubMed 

Google Scholar 
7.Hertel, A. G. et al. Don’t poke the bear: Using tracking data to quantify behavioural syndromes in elusive wildlife. Anim. Behav. 147, 91–104 (2019).Article 

Google Scholar 
8.Harrison, P. M. et al. Personality-dependent spatial ecology occurs independently from dispersal in wild burbot (Lota lota). Behav. Ecol. 26, 483–492 (2015).Article 

Google Scholar 
9.Biro, P. A. & Stamps, J. A. Are animal personality traits linked to life-history productivity?. Trends Ecol. Evol. 23, 361–368 (2008).PubMed 
Article 

Google Scholar 
10.Araújo, M. S., Bolnick, D. I. & Layman, C. A. The ecological causes of individual specialisation. Ecol. Lett. 14, 948–958 (2011).PubMed 
Article 

Google Scholar 
11.Wolf, M. & Weissing, F. J. Animal personalities: Consequences for ecology and evolution. Trends Ecol. Evol. 27, 452–461 (2012).PubMed 
Article 

Google Scholar 
12.Swan, G. J. F., Redpath, S. M., Bearhop, S. & McDonald, R. A. Ecology of problem individuals and the efficacy of selective wildlife management. Trends Ecol. Evol. 32, 518–530 (2017).PubMed 
Article 

Google Scholar 
13.Merrick, M. J. & Koprowski, J. L. Should we consider individual behavior differences in applied wildlife conservation studies?. Biol. Conserv. 209, 34–44 (2017).Article 

Google Scholar 
14.Hertel, A. G. et al. A guide for studying among-individual behavioral variation from movement data in the wild. Mov. Ecol. 8, 1–18 (2020).Article 

Google Scholar 
15.Poulsen, J. R. et al. Ecological consequences of forest elephant declines for Afrotropical forests. Conserv. Biol. 32, 559–567 (2018).PubMed 
Article 

Google Scholar 
16.Poulsen, J. R. et al. Poaching empties critical Central African wilderness of forest elephants. Curr. Biol. 27, R134–R135 (2017).CAS 
PubMed 
Article 

Google Scholar 
17.Maisels, F. et al. Devastating decline of forest elephants in Central Africa. PLoS ONE 8, e59469 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
18.Allen, A. M. & Singh, N. J. Linking movement ecology with wildlife management and conservation. Front. Ecol. Evol. 4, 1–13 (2016).
Google Scholar 
19.Holyoak, M., Casagrandi, R., Nathan, R., Revilla, E. & Spiegel, O. Trends and missing parts in the study of movement ecology. Proc. Natl. Acad. Sci. 105, 19060–19065 (2008).CAS 
PubMed 
Article 
ADS 

Google Scholar 
20.Carbone, C., Cowlishaw, G., Isaac, N. J. B. & Rowcliffe, J. M. How far do animals go? Determinants of day range in mammals. Am. Nat. 165, 290–297 (2005).PubMed 
Article 

Google Scholar 
21.Powell, R. A. & Mitchell, M. S. What is a home range?. J. Mammal. 93, 948–958 (2012).Article 

Google Scholar 
22.Oliver, I., Beattie, A. J. & York, A. Spatial fidelity of plant, vertebrate, and invertebrate assemblages in multiple-use forest in Eastern Australia. Conserv. Biol. 12, 822–835 (1998).Article 

Google Scholar 
23.van Beest, F. M., Vander Wal, E., Stronen, A. V., Paquet, P. C. & Brook, R. K. Temporal variation in site fidelity: Scale-dependent effects of forage abundance and predation risk in a non-migratory large herbivore. Oecologia 52, 409–420 (2002).
Google Scholar 
24.Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

Google Scholar 
25.Ihwagi, F. W. et al. Night-day speed ratio of elephants as indicator of poaching levels. Ecol. Indic. 84, 38–44 (2018).Article 

Google Scholar 
26.Wrege, P. H., Rowland, E. D., Thompson, B. G. & Batruch, N. Use of acoustic tools to reveal otherwise cryptic responses of forest elephants to oil exploration. Conserv. Biol. 24, 1578–1585 (2010).PubMed 
Article 

Google Scholar 
27.Wrege, P. H., Rowland, E. D., Keen, S. & Shiu, Y. Acoustic monitoring for conservation in tropical forests: Examples from forest elephants. Methods Ecol. Evol. 8, 1292–1301 (2017).Article 

Google Scholar 
28.Vanleeuwe, H., Gautier-Hion, A. & Cajani, S. Forest clearings and the conservation of elephants (Loxodonta africana cyclotis) in North East Congo Republic. Pachyderm 24, 46–52 (1997).
Google Scholar 
29.Mcclintock, B. T., Fisheries, A. & Michelot, T. momentuHMM: R package for generalized hidden Markov models of animal movement. Methods Ecol. Evol. 2018, 1518–1530 (2017).
Google Scholar 
30.Vogel, S. M. et al. Exploring movement decisions: Can Bayesian movement-state models explain crop consumption behaviour in elephants (Loxodonta africana)?. J. Anim. Ecol. 89, 1055–1068 (2020).PubMed 
Article 

Google Scholar 
31.Nathan, R. et al. A movement ecology paradigm for unifying organismal movement research. Proc. Natl. Acad. Sci. 105, 19052–19059 (2008).CAS 
PubMed 
Article 
ADS 

Google Scholar 
32.Van Beest, F. M., Rivrud, I. M., Loe, L. E., Milner, J. M. & Mysterud, A. What determines variation in home range size across spatiotemporal scales in a large browsing herbivore?. J. Anim. Ecol. 80, 771–785 (2011).PubMed 
Article 

Google Scholar 
33.Lindstedt, S. L. Home range, time, and body size in mammals. Ecology 67, 413–418 (1986).Article 

Google Scholar 
34.Mills, E. C. et al. Forest elephant movement and habitat use in a tropical forest-grassland mosaic in Gabon. PLoS One 13, e0199387 (2018).35.Bohrer, G., Beck, P. S. S. A., Ngene, S. M., Skidmore, A. K. & Douglas-Hamilton, I. Elephant movement closely tracks precipitation-driven vegetation dynamics in a Kenyan forest-savanna landscape. Mov. Ecol. 2, 2 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
36.Kolowski, J. M. et al. Movements of four forest elephants in an oil concession in Gabon, Central Africa. Afr. J. Ecol. 48, 1134–1138 (2010).Article 

Google Scholar 
37.Laurance, W. F. et al. Impacts of roads and hunting on central African rainforest mammals. Conserv. Biol. 20, 1251–1261 (2006).PubMed 
Article 

Google Scholar 
38.Buij, R. et al. Patch-occupancy models indicate human activity as major determinant of forest elephant Loxodonta cyclotis seasonal distribution in an industrial corridor in Gabon. Biol. Conserv. 135, 189–201 (2007).Article 

Google Scholar 
39.Blake, S. et al. Roadless wilderness area determines forest elephant movements in the Congo Basin. PLoS ONE 3, e3546 (2008).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
40.Torney, C. J., Grant, J., Morrison, T. A., Couzin, I. D. & Levin, S. A. From single steps to mass migration: The problem of scale in the movement ecology of the serengeti wildebeest. Philos. Trans. R. Soc. B Biol. Sci. 373, 2 (2018).
Google Scholar 
41.Roberts, C. P., Cain, J. W. & Cox, R. D. Identifying ecologically relevant scales of habitat selection: Diel habitat selection in elk: Diel. Ecosphere 8, 2 (2017).Article 

Google Scholar 
42.Sannier, C., McRoberts, R. E., Fichet, L. V. & Makaga, E. M. K. Using the regression estimator with Landsat data to estimate proportion forest cover and net proportion deforestation in Gabon. Remote Sens. Environ. 151, 138–148 (2014).Article 
ADS 

Google Scholar 
43.Calenge, C. The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals. Ecol. Modell. 197, 516–519 (2006).Article 

Google Scholar 
44.Hoogenboom, I., Daan, S., Dallinga, J. H. & Schoenmakers, M. Seasonal change in the daily timing of behaviour of the common vole Microtus arvalis. Oecologia 61, 18–31. https://doi.org/10.1007/BF00379084(1984).CAS 
Article 
PubMed 
ADS 

Google Scholar 
45.Polansky, L., Kilian, W. & Wittemyer, G. Elucidating the significance of spatial memory on movement decisions by African savannah elephants using state–space models. Proc. R. Soc. B Biol. Sci. 282, 2 (2015).
Google Scholar 
46.Taylor, L. A. et al. Movement reveals reproductive tactics in male elephants. J. Anim. Ecol. 89, 57–67 (2020).PubMed 
Article 

Google Scholar 
47.WCS, W. C. S.- & University, C. for I. E. S. I. N.-C.-C. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). (2005).48.Hadfield, J. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).Article 

Google Scholar 
49.R Core Team. R: A language and environment for statistical computing. R Found. Stat. Comput. (2019).50.Gelman, A. & Hill, J. Data analysis using regression and multilevel/hierarchical models (Cambridge University Press, 2006).Book 

Google Scholar 
51.Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).Article 

Google Scholar 
52.Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: Convergence Diagnosis and Output Analysis for MCMC. R News https://doi.org/10.1016/s0306-4522(05)00880-8 (2006).Article 

Google Scholar 
53.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar 
54.Houslay, T. M. & Wilson, A. J. Avoiding the misuse of BLUP in behavioural ecology. Behav. Ecol. 28, 948–952 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
55.Houslay, T. M., Vierbuchen, M., Grimmer, A. J., Young, A. J. & Wilson, A. J. Testing the stability of behavioural coping style across stress contexts in the Trinidadian guppy. Funct. Ecol. 32, 424–438 (2018).PubMed 
Article 

Google Scholar 
56.Taylor, L. A. et al. Movement reveals reproductive tactics in male elephants. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13035 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
57.Tucker, M. A. et al. Moving in the anthropocene: Global reductions in terrestrial mammalian movements. Science 359, 466–469 (2018).CAS 
PubMed 
Article 
ADS 

Google Scholar 
58.Yackulic, C. B., Strindberg, S., Maisels, F. & Blake, S. The spatial structure of hunter access determines the local abundance of forest elephants (Loxodonta africana cyclotis). Ecol. Appl. 21, 1296–1307 (2011).PubMed 
Article 

Google Scholar 
59.Blake, S. et al. Fruit, minerals, and forest elephant trails: Do all roads lead to Rome?. Biotropica 36, 392 (2006).
Google Scholar 
60.Campos-Arceiz, A. & Blake, S. Megagardeners of the forest—the role of elephants in seed dispersal. Acta Oecol. 37, 542–553 (2011).Article 
ADS 

Google Scholar 
61.Beirne, C. et al. Climatic and resource determinants of forest elephant movements. Front. Ecol. Evol. 8, 1–14 (2020).Article 

Google Scholar 
62.Morellato, L. P. C. et al. Linking plant phenology to conservation biology. Biol. Conserv. 195, 60–72 (2016).Article 

Google Scholar 
63.Bush, E. R. et al. Rare ground data confirm significant warming and drying in western equatorial Africa. PeerJ 2020, 1–29 (2020).
Google Scholar 
64.Bush, E. R. et al. Long-term collapse in fruit availability threatens Central African forest megafauna. Science 7791, 7791 (2020).
Google Scholar 
65.Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics (Longman, 1996).
Google Scholar 
66.Araya-Ajoy, Y. G., Mathot, K. J. & Dingemanse, N. J. An approach to estimate short-term, long-term and reaction norm repeatability. Methods Ecol. Evol. 6, 1462–1473 (2015).Article 

Google Scholar 
67.McDougall, P. T., Réale, D., Sol, D. & Reader, S. M. Wildlife conservation and animal temperament: Causes and consequences of evolutionary change for captive, reintroduced, and wild populations. Anim. Conserv. 9, 39–48 (2006).Article 

Google Scholar  More

1.Akpomie, K. G. & Dawodu, F. A. Montmorillonite-rice husk composite for heavy metal sequestration from binary aqua media: a novel adsorbent. Trans. R. Soc. South Africa 70(1), 83–88 (2015).Article 

Google Scholar 
2.Ali, A., Saeed, K. & Mabood, F. Removal of chromium (VI) from aqueous medium using chemically modified banana peels as efficient low-cost adsorbent. Alex. Eng. J. 55(3), 2933–2942 (2016).Article 

Google Scholar 
3.Amaku, J. F., Ogundare, S. A., Akpomie, K. G., Ngwu, C. M. & Conradie, J. Sequestered uptake of chromium(VI) by Irvingia gabonensis stem bark extract anchored silica gel. Biomass Conver. Biorefinery 3, 19 (2021).
Google Scholar 
4.Malwade, K., Lataye, D., Mhaisalkar, V., Kurwadkar, S. & Ramirez, D. Adsorption of hexavalent chromium onto activated carbon derived from Leucaena leucocephala waste sawdust: kinetics, equilibrium and thermodynamics. Int. J. Environ. Sci. Technol. 13(9), 2107–2116 (2016).CAS 
Article 

Google Scholar 
5.Belay, A. A. Impacts of chromium from tannery effluent and evaluation of alternative treatment options. J. Environ. Prot. 1(01), 53 (2010).CAS 
Article 

Google Scholar 
6.Das, A. K. Micellar effect on the kinetics and mechanism of chromium(VI) oxidation of organic substrates. Coord. Chem. Rev. 248(1), 81–99 (2004).CAS 
Article 

Google Scholar 
7.Paš, M., Milačič, R., Drašar, K., Pollak, N. & Raspor, P. Uptake of chromium (III) and chromium (VI) compounds in the yeast cell structure. Biometals 17(1), 25–33 (2004).PubMed 
Article 

Google Scholar 
8.Medeiros, M. et al. Elevated levels of DNA–protein crosslinks and micronuclei in peripheral lymphocytes of tannery workers exposed to trivalent chromium. Mutagenesis 18(1), 19–24 (2003).CAS 
PubMed 
Article 

Google Scholar 
9.Tuzen, M.; Elik, A.; Altunay, N., Ultrasound-assisted supramolecular solvent dispersive liquid-liquid microextraction for preconcentration and determination of Cr (VI) in waters and total chromium in beverages and vegetables. J. Mol. Liq. 2021, 329, 115556.10.Gao, H. et al. Improved Adsorption Performance of α-Fe2O3 Modified with Carbon Spheres for Cr (VI) Removal from Aqueous Solution. J. Nanosci. Nanotechnol. 18(2), 1034–1042 (2018).CAS 
PubMed 
Article 

Google Scholar 
11.Amaku, J. F.; Ogundare, S. A.; Akpomie, K. G.; Conradie, J., Enhanced sequestration of chromium (VI) onto spent self-indicating silica gels coated with Harpephyllum caffrum stem bark extract. International Journal of Environmental Analytical Chemistry 2021, 1–17.12.Pagilla, K. R. & Canter, L. W. Laboratory studies on remediation of chromium-contaminated soils. J. Environ. Eng. 125(3), 243–248 (1999).CAS 
Article 

Google Scholar 
13.Ali, J. et al. Separation and preconcentration of trivalent chromium in environmental waters by using deep eutectic solvent with ultrasound-assisted based dispersive liquid-liquid microextraction method. J. Mol. Liq. 291, 111299 (2019).CAS 
Article 

Google Scholar 
14.Aksu, Z., Özer, D., Ekiz, H. I., Kutsal, T. & Çaglar, A. Investigation of biosorption of chromium (VI) on Cladophora crispata in two-staged batch reactor. Environ. Technol. 17(2), 215–220 (1996).CAS 
Article 

Google Scholar 
15.Tiravanti, G., Petruzzelli, D. & Passino, R. Pretreatment of tannery wastewaters by an ion exchange process for Cr(III) removal and recovery. Water Sci. Technol. 36(2), 197–207 (1997).CAS 
Article 

Google Scholar 
16.Seaman, J. C., Bertsch, P. M. & Schwallie, L. In situ Cr (VI) reduction within coarse-textured, oxide-coated soil and aquifer systems using Fe (II) solutions. Environ. Sci. Technol. 33(6), 938–944 (1999).ADS 
CAS 
Article 

Google Scholar 
17.Amaku, J. F. et al. Chrysophyllum albidum stem bark extract coated tillite adsorbent for the uptake of Cr (VI): thermodynamic, kinetic, isotherm, and reusability. Biomass Conver. Biorefinery 2, 1–13 (2021).
Google Scholar 
18.Lyubchik, S. I. et al. Kinetics and thermodynamics of the Cr (III) adsorption on the activated carbon from co-mingled wastes. Colloids Surf. A 242(1), 151–158 (2004).CAS 
Article 

Google Scholar 
19.Demirbas, E., Kobya, M., Senturk, E. & Ozkan, T. Adsorption kinetics for the removal of chromium (VI) from aqueous solutions on the activated carbons prepared from agricultural wastes. Water Sea 30(4), 533–539 (2004).CAS 

Google Scholar 
20.Oliveira, E., Montanher, S., Andrade, A., Nobrega, J. & Rollemberg, M. Equilibrium studies for the sorption of chromium and nickel from aqueous solutions using raw rice bran. Process Biochem. 40(11), 3485–3490 (2005).CAS 
Article 

Google Scholar 
21.Samuel, M. S. et al. Efficient removal of Chromium (VI) from aqueous solution using chitosan grafted graphene oxide (CS-GO) nanocomposite. Int. J. Biol. Macromol. 121, 285–292 (2019).CAS 
PubMed 
Article 

Google Scholar 
22.Samuel, M. S. et al. Preparation of graphene oxide/chitosan/ferrite nanocomposite for Chromium (VI) removal from aqueous solution. Int. J. Biol. Macromol. 119, 540–547 (2018).CAS 
PubMed 
Article 

Google Scholar 
23.Parlayici, S., Eskizeybek, V., Avcı, A. & Pehlivan, E. Removal of chromium (VI) using activated carbon-supported-functionalized carbon nanotubes. J. Nanostruct. Chem. 5(3), 255–263 (2015).CAS 
Article 

Google Scholar 
24.Hu, J., Chen, C., Zhu, X. & Wang, X. Removal of chromium from aqueous solution by using oxidized multiwalled carbon nanotubes. J. Hazard. Mater. 162(2), 1542–1550 (2009).CAS 
PubMed 

Google Scholar 
25.Amaku, J., Ogundare, S., Akpomie, K., Ngwu, C. & Conradie, J. Enhanced chromium (VI) removal by Anacardium occidentale stem bark extract-coated multiwalled carbon nanotubes. Int. J. Environ. Sci. Technol. 6, 1–14 (2021).
Google Scholar 
26.Amaku, J. F. et al. Thermodynamics, kinetics and isothermal studies of chromium (VI) biosorption onto Detarium senegalense stem bark extract coated shale and the regeneration potentials. Int. J. Phytoremed. 5, 1–11 (2021).Article 
CAS 

Google Scholar 
27.Tunali, S., Kiran, I. & Akar, T. Chromium (VI) biosorption characteristics of Neurospora crassa fungal biomass. Miner. Eng. 18(7), 681–689 (2005).CAS 
Article 

Google Scholar 
28.Módenes, A. N. et al. Study of the involved sorption mechanisms of Cr (VI) and Cr (III) species onto dried Salvinia auriculata biomass. Chemosphere 172, 373–383 (2017).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
29.Hlihor, R. M., Figueiredo, H., Tavares, T. & Gavrilescu, M. Biosorption potential of dead and living Arthrobacter viscosus biomass in the removal of Cr(VI): Batch and column studies. Process Saf. Environ. Prot. 108, 44–56 (2017).CAS 
Article 

Google Scholar 
30.Ji, B. et al. Chromium (VI) removal from water using starch coated nanoscale zerovalent iron particles supported on activated carbon. Chem. Eng. Commun. 7, 1–8 (2018).
Google Scholar 
31.Zhu, Y. et al. Removal of hexavalent chromium from aqueous solution by different surface-modified biochars: Acid washing, nanoscale zero-valent iron and ferric iron loading. Biores. Technol. 261, 142–150 (2018).CAS 
Article 

Google Scholar 
32.Zhang, S.-H. et al. Mechanism investigation of anoxic Cr (VI) removal by nano zero-valent iron based on XPS analysis in time scale. Chem. Eng. J. 335, 945–953 (2018).CAS 
Article 

Google Scholar 
33.Amaku, J. F. et al. Adsorption of Cr (VI) onto Azadirachta indica stem bark extract modified dolerite composite adsorbent. Int. J. Environ. Anal. Chem. 7, 1–18 (2021).
Google Scholar 
34.Agarwal, G., Bhuptawat, H. K. & Chaudhari, S. Biosorption of aqueous chromium (VI) by Tamarindus indica seeds. Biores. Technol. 97(7), 949–956 (2006).CAS 
Article 

Google Scholar 
35.Zhang, Y. et al. Malic acid-enhanced chitosan hydrogel beads (mCHBs) for the removal of Cr (VI) and Cu (II) from aqueous solution. Chem. Eng. J. 3, 79 (2018).
Google Scholar 
36.Moussout, H., Ahlafi, H., Aazza, M. & El Akili, C. Performances of local chitosan and its nanocomposite 5% Bentonite/Chitosan in the removal of chromium ions (Cr (VI)) from wastewater. Int. J. Biol. Macromol. 108, 1063–1073 (2018).CAS 
PubMed 
Article 

Google Scholar 
37.Moreira, A. L. D. S. L. et al. Bifunctionalized chitosan: a versatile adsorbent for removal of Cu (II) and Cr (VI) from aqueous solution. Carbohydr. Polym. 201, 218–227 (2018).CAS 
PubMed 
Article 

Google Scholar 
38.Lordi, V. & Yao, N. Molecular mechanics of binding in carbon-nanotube–polymer composites. J. Mater. Res. 15(12), 2770–2779 (2000).ADS 
CAS 
Article 

Google Scholar 
39.Kataura, H. et al. Optical properties of single-wall carbon nanotubes. Synth. Met. 103(1–3), 2555–2558 (1999).CAS 
Article 

Google Scholar 
40.Harris, P. J., Carbon nanotubes and related structures: new materials for the twenty-first century. AAPT: 2004.41.Stahl, H., Appenzeller, J., Martel, R., Avouris, P. & Lengeler, B. Intertube coupling in ropes of single-wall carbon nanotubes. Phys. Rev. Lett. 85(24), 5186 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
42.Dai, H. et al. Electrical transport properties and field effect transistors of carbon nanotubes. NANO 1(01), 1–13 (2006).CAS 
Article 

Google Scholar 
43.Rao, G. P., Lu, C. & Su, F. Sorption of divalent metal ions from aqueous solution by carbon nanotubes: a review. Sep. Purif. Technol. 58(1), 224–231 (2007).CAS 
Article 

Google Scholar 
44.Vuković, G. D. et al. Removal of lead from water by amino modified multi-walled carbon nanotubes. Chem. Eng. J. 173(3), 855–865 (2011).Article 
CAS 

Google Scholar 
45.Gao, Z., Bandosz, T. J., Zhao, Z., Han, M. & Qiu, J. Investigation of factors affecting adsorption of transition metals on oxidized carbon nanotubes. J. Hazard. Mater. 167(1–3), 357–365 (2009).CAS 
PubMed 
Article 

Google Scholar 
46.Xu, D., Tan, X., Chen, C. & Wang, X. Removal of Pb (II) from aqueous solution by oxidized multiwalled carbon nanotubes. J. Hazard. Mater. 154(1–3), 407–416 (2008).CAS 
PubMed 
Article 

Google Scholar 
47.Kanthapazham, R., Ayyavu, C. & Mahendiradas, D. Removal of Pb2+, Ni2+ and Cd2+ ions in aqueous media using functionalized MWCNT wrapped polypyrrole nanocomposite. Desalin. Water Treat. 57(36), 16871–16885 (2016).CAS 

Google Scholar 
48.Wang, Y. et al. Multi-walled carbon nanotubes with selected properties for dynamic filtration of pharmaceuticals and personal care products. Water Res. 92, 104–112 (2016).PubMed 
Article 
CAS 

Google Scholar 
49.Patiño, Y. et al. Adsorption of emerging pollutants on functionalized multiwall carbon nanotubes. Chemosphere 136, 174–180 (2015).ADS 
PubMed 
Article 
CAS 

Google Scholar 
50.Czech, B. & Oleszczuk, P. Sorption of diclofenac and naproxen onto MWCNT in model wastewater treated by H2O2 and/or UV. Chemosphere 149, 272–278 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Howard, J. L. The quartzite problem revisited. J. Geol. 113(6), 707–713 (2005).ADS 
CAS 
Article 

Google Scholar 
52.Olivier, T. T., Moïse, F., Jackson, S. A. & Francis, N. T. A review on traditional uses, phytochemical and pharmacological profiles, spiritual and economic values, and toxicity of Dacryodes Edulis (G. Don) HJ Lam. J. Drug Deliv. Therap. 6(5), 84–90 (2016).
Google Scholar 
53.Ogunmoyole, T., Kade, I., Johnson, O. & Makun, O. Effect of boiling on the phytochemical constituents and antioxidant properties of African pear Dacryodes edulis seeds in vitro. Afr. J. Biochem. Res. 6(8), 105–114 (2012).CAS 
Article 

Google Scholar 
54.Omoregie, E. S. & Okugbo, O. T. In vitro antioxidant activity and phytochemical screening of methanol extracts of Ficus capensis and Dacryodes edulis leaves. Journal of Pharmacy & Bioresources 11(2), 66–75 (2014).Article 

Google Scholar 
55.Niazi, L., Lashanizadegan, A. & Sharififard, H. Chestnut oak shells activated carbon: Preparation, characterization and application for Cr (VI) removal from dilute aqueous solutions. J. Clean. Prod. 185, 554–561 (2018).CAS 
Article 

Google Scholar 
56.Klimaviciute, R., Bendoraitiene, J., Rutkaite, R. & Zemaitaitis, A. Adsorption of hexavalent chromium on cationic cross-linked starches of different botanic origins. J. Hazard. Mater. 181(1–3), 624–632 (2010).CAS 
PubMed 
Article 

Google Scholar 
57.Mishra, S., Chakraborty, T. & Matsagar, V. Dynamic characterization of himalayan quartzite using SHPB. Procedia Eng. 191, 2–9 (2017).Article 

Google Scholar 
58.Torres, P., Manjate, R., Quaresma, S., Fernandes, H. & Ferreira, J. M. D. F. Development of ceramic floor tile compositions based on quartzite and granite sludges. J. Eur. Ceram. Soc. 27(16), 4649–4655 (2007).CAS 
Article 

Google Scholar 
59.Amaral, P. M.; Fernandes, J. C.; Rosa, L. G. In A comparison between X-ray diffraction and petrography techniques used to determine the mineralogical composition of granite and comparable hard rocks, Materials science forum, Trans Tech Publ: 2006; pp 1628-1632.60.Hessel, C. M., Henderson, E. J. & Veinot, J. G. Hydrogen silsesquioxane: a molecular precursor for nanocrystalline Si−SiO2 composites and freestanding hydride-surface-terminated silicon nanoparticles. Chem. Mater. 18(26), 6139–6146 (2006).CAS 
Article 

Google Scholar 
61.Xie, P.-P. et al. Effects of cadmium on bioaccumulation and biochemical stress response in rice (Oryza sativa L.). Ecotoxicol. Environ. Saf. 122, 392–398 (2015).CAS 
PubMed 
Article 

Google Scholar 
62.Wang, G. et al. Removal of Pb (II) from aqueous solutions by Phytolacca americana L. biomass as a low cost biosorbent. Arab. J. Chem. 11(1), 99–110 (2018).CAS 
Article 

Google Scholar 
63.Brunauer, S. & Emmett, P. Chemisorptions of gases on iron synthetic ammonia catalysts1. J. Am. Chem. Soc. 62(7), 1732–1746 (1940).CAS 
Article 

Google Scholar 
64.Xing, Z.; Hébert, R.; Beaucour, A.; Ledésert, B.; Noumowé, A.; Linder, N. In Influence of aggregate’s nature on their instability at elevated temperature, 2nd International RILEM Workshop on Concrete Spalling due to Fire Exposure, RILEM Publications SARL: 2011; pp 149–156.65.Szabó, M. et al. Equilibria and kinetics of chromium (VI) speciation in aqueous solution–a comprehensive study from pH 2 to 11. Inorg. Chim. Acta 472, 295–301 (2018).Article 
CAS 

Google Scholar 
66.Saygi, K. O., Tuzen, M., Soylak, M. & Elci, L. Chromium speciation by solid phase extraction on Dowex M 4195 chelating resin and determination by atomic absorption spectrometry. J. Hazard. Mater. 153(3), 1009–1014 (2008).CAS 
PubMed 
Article 

Google Scholar 
67.Shanmugalingam, A. & Murugesan, A. Removal of hexavalent chromium by adsorption on microwave assisted activated carbon prepared from stems of Leucas Aspera. Z. Phys. Chem. 232(4), 489–506 (2018).ADS 
CAS 
Article 

Google Scholar 
68.Belachew, N. & Hinsene, H. Preparation of cationic surfactant-modified kaolin for enhanced adsorption of hexavalent chromium from aqueous solution. Appl. Water Sci. 10(1), 38 (2020).ADS 
CAS 
Article 

Google Scholar 
69.Gaikwad, M. S. & Balomajumder, C. Removal of Cr (VI) and fluoride by membrane capacitive deionization with nanoporous and microporous Limonia acidissima (wood apple) shell activated carbon electrode. Sep. Purif. Technol. 195, 305–313 (2018).CAS 
Article 

Google Scholar 
70.Altundogan, H. S. Cr(VI) removal from aqueous solution by iron (III) hydroxide-loaded sugar beet pulp. Process Biochem. 40(3), 1443–1452 (2005).CAS 
Article 

Google Scholar 
71.Demiral, H., Demiral, I., Tümsek, F. & Karabacakoğlu, B. Adsorption of chromium (VI) from aqueous solution by activated carbon derived from olive bagasse and applicability of different adsorption models. Chem. Eng. J. 144(2), 188–196 (2008).CAS 
Article 

Google Scholar 
72.Ma, M., Lu, Y., Chen, R., Ma, L. & Wang, Y. Hexavalent chromium removal from water using heat-acid activated red mud. Open J. Appl. Sci. 4(05), 275 (2014).ADS 
Article 
CAS 

Google Scholar 
73.Aslani, H., Kosari, T. E., Naseri, S., Nabizadeh, R. & Khazaei, M. Hexavalent chromium removal from aqueous solution using functionalized chitosan as a novel nano-adsorbent: modeling and optimization, kinetic, isotherm, and thermodynamic studies, and toxicity testing. Environ. Sci. Pollut. Res. 2, 1–15 (2018).
Google Scholar 
74.Gao, H. et al. Characterization of Cr (VI) removal from aqueous solutions by a surplus agricultural waste—rice straw. J. Hazard. Mater. 150(2), 446–452 (2008).CAS 
PubMed 
Article 

Google Scholar 
75.Chagas, P. M. B. et al. Nanostructured oxide stabilized by chitosan: Hybrid composite as an adsorbent for the removal of chromium (VI). J. Environ. Chem. Eng. 6(1), 1008–1019 (2018).CAS 
Article 

Google Scholar 
76.Qiu, J. et al. Adsorption of Cr (VI) using silica-based adsorbent prepared by radiation-induced grafting. J. Hazard. Mater. 166(1), 270–276 (2009).CAS 
PubMed 
Article 

Google Scholar 
77.Hamza, I. A. A., Martincigh, B. S., Ngila, J. C. & Nyamori, V. O. Adsorption studies of aqueous Pb(II) onto a sugarcane bagasse/multi-walled carbon nanotube composite. Phys. Chem. Earth Parts A/B/C 66, 157–166 (2013).ADS 
Article 

Google Scholar 
78.Liu, Y. & Liu, Y.-J. Biosorption isotherms, kinetics and thermodynamics. Sep. Purif. Technol. 61(3), 229–242 (2008).CAS 
Article 

Google Scholar  More

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. Similarly, a pairwise t-test was used to test the effect of Cx. quinquefasciatus larvae on Aedes mosquito oviposition choice.To examine the effect of water sources on egg hatching, the t-test was used to analyze the egg hatching rate. The analysis of larval life table study data focused on pupation rates and larval-to-pupal development times. The pupation rate was calculated as the proportion of first-instar larvae that molted into pupae. The effect of water sources and larval food supplementation on pupation rate was analyzed using non-parametric Wilcoxon test. The t-test was used to analyze the duration of larval-to-pupal development. Kaplan–Meier survival analysis was used to determine the effects of food supplementation and water source on larval development for each species, and the log-rank test was conducted to determine their statistical significance. All statistical analyses were performed using JMP software (JMP 14.2, SAS Institute Inc.). More

1.Bezemer, T. M. & van Dam, N. M. Linking aboveground and belowground interactions via induced plant defenses. Trends Ecol. Evol. 20, 617–624. https://doi.org/10.1016/j.tree.2005.08.006 (2005).Article 
PubMed 

Google Scholar 
2.Kaplan, I. et al. Physiological integration of roots and shoots in plant defense strategies links above-and belowground herbivory. Ecol. Lett. 11, 841–851. https://doi.org/10.1111/j.1461-0248.2008.01200.x (2008).Article 
PubMed 

Google Scholar 
3.Huang, W., Siemann, E., Carrillo, J. & Ding, J. Below-ground herbivory limits induction of extrafloral nectar by above-ground herbivores. Ann. Bot. 115, 841–846. https://doi.org/10.1093/aob/mcv011 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Omer, A. D., Thaler, J. S., Granett, J. & Karban, R. Jasmonic acid induced resistance in grapevines to a root and leaf feeder. J. Econ. Entomol. 93, 840–845. https://doi.org/10.1603/0022-0493-93.3.840 (2000).CAS 
Article 
PubMed 

Google Scholar 
5.Yamawo, A., Ohsaki, H. & Cahill, J. F. Jr. Damage to leaf veins suppresses root foraging precision. Am. J. Bot. 106, 1126–1130. https://doi.org/10.1002/ajb2.1338 (2019).Article 
PubMed 

Google Scholar 
6.Heil, M. & Karban, R. Explaining evolution of plant communication by airborne signals. Trends Ecol. Evol. 25, 137–144. https://doi.org/10.1016/j.tree.2009.09.010 (2010).Article 
PubMed 

Google Scholar 
7.Karban, R., Yang, L. J. & Edwards, K. F. Volatile communication between plants that affects herbivory: A meta-analysis. Ecol. Lett. 17, 44–52. https://doi.org/10.1111/ele.12205 (2014).Article 
PubMed 

Google Scholar 
8.Yoneya, K. & Takabayashi, J. Plant-plant communication mediated by airborne signals: Ecological and plant physiological perspectives. Plant Biotechnol. 31, 409–416. https://doi.org/10.5511/plantbiotechnology.14.0827a (2014).CAS 
Article 

Google Scholar 
9.Morrell, K. & Kessler, A. Plant communication in a widespread goldenrod: Keeping herbivores on the move. Funct. Ecol. 31, 1049–1061. https://doi.org/10.1111/1365-2435.12793 (2017).Article 

Google Scholar 
10.Arimura, G. et al. Herbivory-induced volatiles elicit defence genes in lima bean leaves. Nature 406, 512–515 (2000).ADS 
CAS 
Article 

Google Scholar 
11.Shiojiri, K. et al. Weeding volatiles reduce leaf and seed damage to field-grown soybeans and increase seed isoflavones. Sci. Rep. 7, 1–8. https://doi.org/10.1038/srep41508 (2017).CAS 
Article 

Google Scholar 
12.Karban, R., Shiojiri, K., Huntzinger, M. & McCall, A. C. Damage-induced resistance in sagebrush: Volatiles are key to intra- and interplant communication. Ecology 87, 922–930. https://doi.org/10.1890/0012-9658(2006)87[922:drisva]2.0.co;2 (2006).Article 
PubMed 

Google Scholar 
13.Kikuta, Y. et al. Specific regulation of pyrethrin biosynthesis in Chrysanthemum cinerariaefolium by a blend of volatiles emitted from artificially damaged conspecific plants. Plant Cell Physiol. 52, 588–596. https://doi.org/10.1093/pcp/pcr017 (2011).CAS 
Article 
PubMed 

Google Scholar 
14.Karban, R., Baldwin, I. T., Baxter, K. J., Laue, G. & Felton, G. W. Communication between plants: Induced resistance in wild tobacco plants following clipping of neighboring sagebrush. Oecologia 125, 66–71. https://doi.org/10.1007/PL00008892 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
15.Karban, R., Huntzinger, M. & McCall, A. C. The specificity of eavesdropping on sagebrush by other plants. Ecology 85, 1845–1852. https://doi.org/10.1890/03-0593 (2004).Article 

Google Scholar 
16.Shiojiri, K. et al. Exposure to artificially damaged goldenrod volatiles increases saponins in seeds of field-grown soybean plants. Phytochem. Lett. 36, 7–10. https://doi.org/10.1016/j.phytol.2020.01.014 (2020).CAS 
Article 

Google Scholar 
17.Glinwood, R., Ninkovic, V. & Pettersson, J. Chemical interaction between undamaged plants—Effects on herbivores and natural enemies. Phytochemistry 72, 1683–1689. https://doi.org/10.1016/j.phytochem.2011.02.010 (2011).CAS 
Article 
PubMed 

Google Scholar 
18.Lokesh, R. A. V. I., Manasvi, V. & Lakshmi, B. P. Antibacterial and antioxidant activity of saponin from Abutilon indicum leaves. Asian J. Pharm. Clin. Res. 9, 344–347. https://doi.org/10.22159/ajpcr.2016.v9s3.15064 (2016).CAS 
Article 

Google Scholar 
19.Raji, P., Samrot, A. V., Keerthana, D. & Karishma, S. Antibacterial activity of alkaloids, flavonoids, saponins and tannins mediated green synthesised silver nanoparticles against Pseudomonas aeruginosa and Bacillus subtilis. J. Cluster Sci. 30, 881–895. https://doi.org/10.1007/s10876-019-01547-2 (2019).CAS 
Article 

Google Scholar 
20.Toth, R., Toth, D. & Starke, D. Vesicular-arbuscular mycorrhizal colonization in Zea mays affected by breeding for resistance to fungal pathogens. Can. J. Bot. 68, 1039–1044. https://doi.org/10.1139/b90-131 (1990).Article 

Google Scholar 
21.Matyssek, R. et al. The plant’s capacity in regulating resource demand. Plant Biol. 7, 560–580. https://doi.org/10.1055/s-2005-872981 (2005).CAS 
Article 
PubMed 

Google Scholar 
22.Brundrett, M. C. Coevolution of roots and mycorrhizas of land plants. New Phytol. 154, 275–304. https://doi.org/10.1046/j.1469-8137.2002.00397.x (2002).Article 
PubMed 

Google Scholar 
23.Kiers, E. T., Rousseau, R. A., West, S. A. & Denison, R. F. Host sanctions and the legume-rhizobium mutualism. Nature 425, 78–81. https://doi.org/10.1038/nature01931 (2003).ADS 
CAS 
Article 

Google Scholar 
24.Walters, D. R. Plant Defense: Warding Off Attack by Pathogens, Herbivores, and Parasitic Plants (Blackwell Publishing, 2011).
Google Scholar 
25.Szakiel, A., Pączkowski, C. & Henry, M. Influence of environmental biotic factors on the content of saponins in plants. Phytochem. Rev. 10, 493–502. https://doi.org/10.1007/s11101-010-9164-2 (2011).CAS 
Article 

Google Scholar 
26.Singh, B. & Kaur, A. Control of insect pests in crop plants and stored food grains using plant saponins: A review. LWT 87, 93–101. https://doi.org/10.1016/j.lwt.2017.08.077 (2018).CAS 
Article 

Google Scholar 
27.Barton, K. E. & Koricheva, J. The ontogeny of plant defense and herbivory: Characterizing general patterns using meta-analysis. Am. Nat. 175, 481–493. https://doi.org/10.1086/650722 (2010).Article 
PubMed 

Google Scholar 
28.Feeny PP. Seasonal changes in oak leaf tannins and nutrients as a cause of spring feeding by winter moth caterpillars. Ecology 51, 565–581 https://doi.org/10.2307/1934037 (1970).Article 

Google Scholar 
29.Dudt, J. F. & Shure, D. J. The influence of light and nutrients on foliar phenolics and insect herbivory. Ecology 75, 86–98. https://doi.org/10.2307/1939385 (1994).Article 

Google Scholar 
30.Julkunen-Tiitto, R. Phenolic constituents in the leaves of northern willows: Methods for the analysis of certain phenolics. Asian J. Pharm. Clin. Res. 33, 213–217. https://doi.org/10.1021/jf00062a013 (1985).CAS 
Article 

Google Scholar 
31.Folin, O. & Denis, W. A colorimetric method for the determination of phenols (and phenol derivatives) in urine. J. Biol. Chem. 22, 305–308 (1915).CAS 
Article 

Google Scholar 
32.Huang, D., Ou, B. & Prior, R. L. The chemistry behind antioxidant capacity assays. J. Agric. Food Chem. 53, 1841–1856. https://doi.org/10.1021/jf030723c (2005).CAS 
Article 
PubMed 

Google Scholar 
33.Mukai, T., Horie, H. & Goto, T. A simple method for determining saponin in tea seed. Chagyo Kenkyu Hokoku 75, 29–31 (1992) (Japanese with English abstract).CAS 
Article 

Google Scholar 
34.Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. A. & Smith, F. A colorimetric method for the determination of sugars. Nature 168, 167–167 (1951).ADS 
CAS 
Article 

Google Scholar 
35.Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. T. & Smith, F. Colorimetric method for determination of sugars and related substances. Anal. Chem. 28, 350–356. https://doi.org/10.1021/ac60111a017 (1956).CAS 
Article 

Google Scholar 
36.Harad, Y. Cation and anion exchange capacity of soil background and methods. 302 Jpn. J. Soil Scie. Plant Nutr. 55, 273–283. 303 (1984) (in Japanese).
37.R Development Core Team. R A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
38.De Geyter, E., Lambert, E., Geelen, D. & Smagghe, G. Novel advances with plant saponins as natural insecticides to control pest insects. Pest Technol. 1, 96–105 (2007).
Google Scholar 
39.Pankhurst, C. E. & Sprent, J. I. Effects of water stress on the respiratory and nitrogen-fixing activity of soybean root nodules. J. Exp. Bot. 26, 287–304. https://doi.org/10.1093/jxb/26.2.287 (1975).CAS 
Article 

Google Scholar 
40.Sparg, S., Light, M. E. & Van Staden, J. Biological activities and distribution of plant saponins. J. Ethnopharmacol. 94, 219–243. https://doi.org/10.1016/j.jep.2004.05.016 (2004).CAS 
Article 
PubMed 

Google Scholar 
41.Saha, S., Walia, S., Kumar, J., Dhingra, S. & Parmar, B. S. Screening for feeding deterrent and insect growth regulatory activity of triterpenic saponins from Diploknema butyracea and Sapindus mukorossi. J. Agric. Food Chem. 58, 434–440. https://doi.org/10.1021/jf902439m (2010).CAS 
Article 
PubMed 

Google Scholar 
42.Hoagland, R. E., Zablotowicz, R. M. & Oleszek, W. A. Effects of alfalfa saponins on in vitro physiological activity of soil and rhizosphere bacteria. J. Crop. Prod. 4, 349–361. https://doi.org/10.1300/J144v04n02_16 (2001).CAS 
Article 

Google Scholar 
43.Killeen, G. F. et al. Antimicrobial saponins of Yucca schidigera and the implications of their in vitro properties for their in vivo impact. J. Agric. Food Chem. 46, 3178–3186. https://doi.org/10.1021/jf970928j (1998).CAS 
Article 

Google Scholar 
44.Sugiyama, A. The soybean rhizosphere: Metabolites, microbes, and beyond—A review. J. Adv. Res. 19, 67–73. https://doi.org/10.1016/j.jare.2019.03.005 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Lucas-Barbosa, D. Integration studies on plant-pollinator and plant–herbivore interactions. Trends Plant Sci. 21, 125–133. https://doi.org/10.1016/j.tplants.2015.10.013 (2016).CAS 
Article 
PubMed 

Google Scholar 
46.De Deyn, G. B., Raaijmakers, C. E. & Van der Putten, W. H. Plant community development is affected by nutrients and soil biota. J. Ecol. 92(5), 824–834. https://doi.org/10.1111/j.0022-0477.2004.00924.x (2004).Article 

Google Scholar 
47.Knelman, J. E. et al. Nutrient addition dramatically accelerates microbial community succession. PLoS ONE 9(7), e102609. https://doi.org/10.1371/journal.pone.0102609 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
48.Yoneya, K. & Takeshi, M. Co-evolution of foraging behaviour in herbivores and their natural enemies predicts multifunctionality of herbivore-induced plant volatiles. Funct. Ecol. 29, 451–461. https://doi.org/10.1111/1365-2435.12398 (2015).Article 

Google Scholar 
49.Karban, R. Plant communication increases heterogeneity in plant phenotypes and herbivore movement. Funct. Ecol. 31, 990–991. https://doi.org/10.1111/1365-2435.12806 (2017).Article 

Google Scholar  More

1.May, R. M. Thresholds and breakpoints in ecosystems with a multiplicity of stable states. Nature 269, 471–477 (1977).Article 

Google Scholar 
2.Scheffer, M., Carpenter, S., Foley, J., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Archer, S., Schimel, D. S. & Holland, E. A. Mechanisms of shrubland expansion: land use, climate, or carbon dioxide. Clim. Change 29, 91–99 (1995).Article 

Google Scholar 
4.Maher, E. L. & Germino, M. J. Microsite variation among conifer species during seedling establishment at alpine treeline. Ecoscience 13, 334–341 (2006).Article 

Google Scholar 
5.Knapp, A. K. et al. Shrub encroachment in North American grasslands: shifts in growth form dominance alters control of ecosystem carbon inputs. Glob. Change Biol. 14, 615–623 (2008).Article 

Google Scholar 
6.McKee, K. L. & Rooth, J. E. Where temperate meets tropical: multi-factorial effects of elevated CO2, nitrogen enrichment, and competition on a mangrove-salt marsh community. Glob. Change Biol. 14, 971–984 (2008).Article 

Google Scholar 
7.Huang, H., Zinnert, J. C., Wood, L. K., Young, D. R. & D’Odorico, P. Non-linear shift from grassland to shrubland in temperate barrier islands. Ecology 99, 1671–1681 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Huang, H., Anderegg, L. D. L., Dawson, T. E., Mote, S. & D’Odorico, P. Critical transition to woody plant dominance through microclimate feedbacks in North American coastal ecosystems. Ecology 101, e03107 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Huenneke, L. F., Anderson, J. P., Remmenga, M. & Schlesinger, W. H. Desertification alters patterns of aboveground net primary production in Chihuahuan ecosystems. Glob. Change Biol. 8, 247–264 (2002).Article 

Google Scholar 
10.Li, J., Okin, G. S., Hartman, L. J. & Epstein, H. E. Quantitative assessment of wind erosion and soil nutrient loss in desert grasslands of southern New Mexico, USA. Biogeochemistry 85, 317–332 (2007).Article 

Google Scholar 
11.D’Odorico, P., Okin, G. S. & Bestelmeyer, B. T. A synthetic review of feedbacks and drivers of shrub encroachment in arid grasslands. Ecohydrology 5, 520–530 (2012).Article 

Google Scholar 
12.Van Auken, O. Shrub invasions of North American semiarid grasslands. Annu. Rev. Ecol. Evol. Syst. 31, 197–215 (2000).Article 

Google Scholar 
13.Sankaran, M. et al. Determinants of woody cover in African savannas. Nature 438, 846–849 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Gehrig-Fasel, J., Guisan, A. & Zimmermann, N. E. Tree line shifts in the Swiss Alps: climate change or land abandonment? J. Veg. Sci. 18, 571–582 (2007).Article 

Google Scholar 
15.Cavanaugh, K. C. et al. Poleward expansion of mangroves is a threshold response to decreased frequency of extreme cold events. Proc. Natl Acad. Sci. USA 111, 723–727 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.D’Odorico, P. et al. Vegetation–microclimate feedbacks in woodland–grassland ecotones. Glob. Ecol. Biogeogr. 22, 364–379 (2013).Article 

Google Scholar 
17.He, Y., D’Odorico, P. & De Wekker, S. F. The relative importance of climate change and shrub encroachment on nocturnal warming in the Southwestern United States. Int. J. Climatol. 35, 475–480 (2014).Article 

Google Scholar 
18.He, Y., D’Odorico, P., De Wekker, S. F., Fuentes, J. D. & Litvak, M. On the impact of shrub encroachment on microclimate conditions in the northern Chihuahuan desert. J. Geophys. Res. Atmos. 115, D21120 (2010).Article 

Google Scholar 
19.Ives, A. R. & Carpenter, S. R. Stability and diversity of ecosystems. Science 317, 58–62 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Ratajczak, Z. et al. Abrupt change in ecological systems: inference and diagnosis. Trends Ecol. Evol. 33, 513–526 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Sugihara, G. & May, R. M. Applications of fractals in ecology. Trends Ecol. Evol. 5, 79–86 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Rodriguez-Iturbe, I. & Rinaldo, A. Fractal River Basins: Chance and Self- Organization. (Cambridge University Press, New York, 1997).24.Majumder, S., Tamma, K., Ramaswamy, S. & Guttal, V. Inferring critical thresholds of ecosystem transitions from spatial data. Ecology 100, e02722 (2019).PubMed 
Article 

Google Scholar 
25.Staver, A. C., Asner, G. P., Rodriguez-Iturbe, I., Levin, S. A. & Smit, I. Spatial patterning among savanna trees in high resolution, spatially extensive data. Proc. Natl Acad. Sci. USA 116, 10685 (2019).Article 
CAS 

Google Scholar 
26.Kéfi, S. et al. Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems. Nature 449, 213–217 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Kéfi, S. et al. Robust scaling in ecosystems and the meltdown of patch size distributions before extinction. Ecol. Lett. 14, 29–35 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
28.Weissmann, H., Kent, R., Michael, Y. & Shnerb, N. M. Empirical analysis of vegetation dynamics and the possibility of a catastrophic desertification transition. PLoS ONE 12, e0189058 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
29.Sankaran, S., Majumder, S., Viswanathan, A. & Guttal, V. Clustering and correlations: inferring resilience from spatial patterns in ecosystems. Methods Ecol. Evol. 10, 2079–2089 (2019).Article 

Google Scholar 
30.Zinnert, J. C. et al. Spatial–temporal dynamics in barrier island upland vegetation: the overlooked coastal landscape. Ecosystems 19, 685–697 (2016).CAS 
Article 

Google Scholar 
31.Thompson, J. A., Zinnert, J. C. & Young, D. R. Immediate effects of microclimate modification enhance native shrub encroachment. Ecosphere 8, e01687 (2017).Article 

Google Scholar 
32.Wood, L. K., Hays, S. & Zinnert, J. C. Decreased temperature variance associated with biotic composition enhances coastal shrub encroachment. Sci. Rep. 10, 8210 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Stauffer, D. & Aharony, A. Introduction to Percolation Theory (Taylor and Francis, London, 1985).34.Taubert, F. et al. Global patterns of tropical forest fragmentation. Nature 554, 519–522 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Scanlon, T. M., Caylor, K. K., Levin, S. A. & Rodriguez-Iturbe, I. Positive feedbacks promote power-law clustering of Kalahari vegetation. Nature 449, 209–212 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Kéfi, S., Rietkerk, M., van Baalen, M. & Loreau, M. Local facilitation, bistability and transitions in arid ecosystems. Theor. Popul. Biol. 71, 367–379 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
37.Berdugo, M., Kéfi, S., Soliveres, S. & Maestre, F. T. Plant spatial patterns identify alternative ecosystem multifunctionality states in global drylands. Nat. Ecol. Evol. 1, 3 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
38.Dakos, V., Kéfi, S., Rietkerk, M., van Nes, E. H. & Scheffer, M. Slowing down in spatially patterned ecosystems at the brink of collapse. Am. Nat. 177, E153–E166 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Weerman, E. J. et al. Changes in diatom patch-size distribution and degradation in a spatially self-organized intertidal mudflat ecosystem. Ecology 93, 608–618 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Kéfi, S., Dakos, V., Scheffer, M., Van Nes, E. H. & Rietkerk, M. Early warning signals also precede non-catastrophic transitions. Oikos 122, 641–648 (2012).Article 

Google Scholar 
41.van Belzen, J. et al. Vegetation recovery in tidal marshes reveals critical slowing down under increased inundation. Nat. Commun. 8, 15811 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
42.USACE-TEC & JALBTCX. Hyperspectral imagery for Hog Island, VA, 2013 ver 7. Environmental Data Initiative. https://doi.org/10.6073/pasta/6a5cc305e93c2baf9283facee688c504 (2018).43.Young, D. R. et al. Cross-scale patterns in shrub thicket dynamics in the Virginia barrier complex. Ecosystems 10, 854–863 (2007).Article 

Google Scholar 
44.Young, D. R., Shao, G. & Porter, J. H. Spatial and temporal growth dynamics of barrier island shrub thickets. Am. J. Bot. 82, 638–645 (1995).Article 

Google Scholar 
45.McGarigal, K., Cushman, S. A. & Ene, E. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps (University of Massachusetts at Amherst, MA, 2012).46.Clauset, A., Shalizi, C. R. & Newman, M. E. J. Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009). 2009.Article 

Google Scholar 
47.Hayden, B. P. Ecosystem feedbacks on climate at the landscape scale. Philos. Trans. R. Soc. B, Biol. Sci. 353, 5–18 (1998).Article 

Google Scholar  More

We untangled how the direct and indirect paths of forest cover and water quality variables interact and shape anopheline assemblages in two seasons. Although previous studies determined how environmental variables at different spatial extents affected anopheline distributions in Amazônia, most studies focused on a single effect of an environmental variable or focused on single habitat types (terrestrial or aquatic)22,23,41,42. Our most important finding is that seasonality modulates the direct and indirect effects of forest cover on Amazônian anopheline larval distributions. In particular, we found that forest cover had stronger direct and indirect influence on larval anopheline assemblage composition in the rainy season than the dry season.The different paths and strengths of forest cover influences on anopheline assemblages during the rainy and dry seasons can be associated with the responses of adults and larvae to forest characteristics. 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