Philippa Kaur

Marine microbiome is considered as the largest environment on earth which has many secrets concealed into it1,2. Many marine microbes play a key role in biogeochemical cycles. However, high proportions of microbes remain uncultured in vitro3 and so instead of analysing the microbes individually, cultivation-independent genome-level characterization methods notably single-cell genomics and metagenomics are frequently being applied for microbiome analysis4. Amplicon sequencing based cultivation-independent studies are enriching the microbial diversity knowledge of various hitherto less studied environmental niche, specifically within the marine resources. However, amplicon analysis is just a preliminary step in metagenomics as it focuses only on one gene for the community diversity assessment.With the view of studying the marine microbial community for determination of its composition in terms of diversity as well as function, whole metagenomics has become the preferred approach. Recently, it has been realized that the actual understanding of metagenomics data can be obtained by individual genome binning, which eventually also enhances the microbial genome database5. This requires use of various complex computational algorithms including those relying on previous data findings viz., the supervised classifiers and the unsupervised classifiers that rely on sequence specific features like the GC content, k-mer frequency and coverage estimation for binning the genomes. Most of the recently developed tools for binning include a combined approach of both the algorithms6. Binning aids in revealing the link between the potential functional genes in a given microbiome to its taxonomy.The unique properties of the Gulfs of Kathiawar Peninsula like extreme tidal variations, different sediment texture and physicochemical variations make them an ideal place for studying the microbial diversity. Varied onshore anthropogenic activities may have imparted unique features to the microflora of the Gulfs. Study of microbial diversity and functions in the mentioned Gulfs have largely been focused on cultivation based approaches and very few molecular studies have been conducted on the shore sediments. Additionally, the presence of several on-shore industries like fertilizer, chemicals, oil refineries, power plants and ASSBRY (Alang Ship Breaking Yard) may have also influenced the deeper sediment microbiome leading to their variable gene profile7. Our previous insights into the pelagic sediment resistome profile by metagenomics approach have shown that the deeper sediments, earlier thought to be primeval are actually hosting microbes with a concerning number of resistance genes7,8. This acted as a propeller to the present study wherein we tried to look deeper into the metagenomics data of the samples collected from the Gulfs of Kathiawar Peninsula and a sample from the Arabian Sea by sorting individual prokaryoplankton genomes from the data using the binning approach.We successfully reconstructed 309 Metagenome Assembled Genomes (MAGs) from the nine sediment metagenomics sequences (Table 1) from Gulf of Khambhat (GOC), Gulf of Kutch (GOK) and Arabian Sea (A) by differential coverage approach and considering the GC percent and tetranucleotide frequencies. Out of the 309 MAGs, 39 were archaeal genomes (Online-only Table 1) and 270 were bacterial genomes (Online-only Table 2). Seventy-one were high quality drafts with a completeness of ≥90% and contamination More

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School of Environmental Studies, University of Victoria, Victoria, British Columbia, CanadaNancy ShackelfordEcology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USANancy Shackelford, Nichole Barger, Julie E. Larson & Katharine L. SudingDepartamento de Ecologia, Universidade Federal do Rio Grande do Norte, Natal, BrazilGustavo B. PaternoDepartment of Ecology and Ecosystem Management, Restoration Ecology Research Group, Technical University of Munich, Freising, GermanyGustavo B. PaternoUS Geological Survey, Southwest Biological Science Center, Moab, UT, USADaniel E. Winkler & Stephen E. FickSchool of Biological Sciences, The University of Western Australia, Crawley, Western Australia, AustraliaTodd E. EricksonKings Park Science, Department of Biodiversity Conservation and Attractions, Kings Park, Western Australia, AustraliaTodd E. Erickson & Peter J. GolosDepartment of Biology, University of Nevada, Reno, Reno, NV, USAElizabeth A. LegerUSDA Agricultural Research Service, Eastern Oregon Agricultural Research Center, Burns, OR, USALauren N. Svejcar, Chad S. Boyd & Kirk W. DaviesCollege of Science and Engineering, Flinders University, Bedford Park, South Australia, AustraliaMartin F. BreedDepartment of Animal and Range Sciences, New Mexico State University, Las Cruces, NM, USAAkasha M. FaistSchool of Natural Sciences and ARC Training Centre for Forest Value, University of Tasmania, Hobart, Tasmania, AustraliaPeter A. HarrisonProgram in Ecology, University of Wyoming, Laramie, WY, USAMichael F. CurranUSDA FS – Southern Research Station, Research Triangle Park, NC, USAQinfeng GuoDepartment of Nature Conservation and Landscape Planning, Anhalt University of Applied Sciences, Bernburg, GermanyAnita Kirmer & Sandra DullauSchool of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USADarin J. LawDepartment of Agricultural Sciences, South Eastern Kenya University, Kitui, KenyaKevin Z. MgangaUS Geological Survey, Southwest Biological Science Center, Flagstaff, AZ, USASeth M. Munson & Hannah L. FarrellUS Department of Agriculture – Agricultural Research Service Rangeland Resources and Systems Research Unit, Fort Collins, CO, USALauren M. PorenskyInstituto Nacional de Tecnología Agropecuaria, Estación Experimental Agropecuaria Catamarca, Catamarca, ArgentinaR. Emiliano QuirogaCátedra de Manejo de Pastizales Naturales, Facultad de Ciencias Agrarias, Universidad Nacional de Catamarca, Catamarca, ArgentinaR. Emiliano QuirogaMTA-DE Lendület Functional and Restoration Ecology Research Group, Debrecen, HungaryPéter TörökTennessee Department of Environment and Conservation, Division of Water Resources, Nashville, TN, USAClaire E. WainwrightHirola Conservation Programme, Nairobi, KenyaAli AbdullahiUSDA Natural Resources Conservation Service, Merced Field Office, Merced, CA, USAMatt A. BahmNational Park Service, Southeast Utah Group, Moab, UT, USAElizabeth A. BallengerThe Nature Conservancy of Oregon, Burns, OR, USAOwen W. BaughmanPlant Conservation Unit, Biological Sciences, University of Cape Town, Rondebosch, South AfricaCarina BeckerUniversity of Castilla-La Mancha, Campus Universitario, Albacete, SpainManuel Esteban Lucas-BorjaUniversity of Northern British Columbia, 3333 University Way, Prince George, British Columbia, CanadaCarla M. Burton & Philip J. BurtonInstitute of Applied Sciences, Malta College for Arts, Sciences and Technology, Fgura, MaltaEman Calleja & Alex CaruanaPlant Conservation Unit, Department of Biological Sciences, University of Cape Town, Rondebosch, South AfricaPeter J. CarrickUSDA, Agricultural Research Service, Great Basin Rangelands Research Unit, Reno, NV, USACharlie D. ClementsLendület Seed Ecology Research Group, Institute of Ecology and Botany, Centre for Ecological Research, Debrecen, HungaryBalázs Deák, Réka Kiss & Orsolya ValkóMurrang Earth Sciences, Ngunnawal Country, Canberra, Australian Capital Territory, AustraliaJessica DrakeGreat Ecology, Denver, CO, USAJoshua EldridgeUSDA-ARS Pest Management Research Unit, Northern Plains Agricultural Research Laboratory, Sidney, MT, USAErin EspelandGerman Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, GermanyMagda GarbowskiDepartment of Ecology, Brandenburg University of Technology, Cottbus, GermanyEnrique G. de la RivaBiodiversity Management Branch, Environmental Resource Management Department, Cape Town, South AfricaPenelope A. GreyGreening Australia, Melbourne, Victoria, AustraliaBarry HeydenrychDepartment of Conservation Ecology & Entomology, Stellenbosch University, Stellenbosch Central, Stellenbosch, South AfricaPatricia M. HolmesNatural Resource Management and Environmental Sciences, Cal Poly State University, San Luis Obispo, CA, USAJeremy J. JamesDepartment of Biology, University of Nebraska-Kearney, Kearney, NE, USAJayne Jonas-BrattenNegaunee Institute for Plant Conservation Science and Action, Chicago Botanic Garden, Glencoe, IL, USAAndrea T. KramerDepartment of Botany, University of Granada, Granada, SpainJuan LoriteInteruniversity Institute for Earth System Research, University of Granada, Granada, SpainJuan LoriteNew Zealand Department of Conservation, Christchurch, New ZealandC. Ellery MayenceDepartamento de Biología y Geología, Física y Química inorgánica, ESCET, Universidad Rey Juan Carlos, Madrid, SpainLuis Merino-MartínÖMKi – Research Institute of Organic Agriculture, Budapest, HungaryTamás MigléczHadison Park, Kimberley, South AfricaSuanne Jane MiltonWolwekraal Conservation and Research Organisation (WCRO), Prince Albert, South AfricaSuanne Jane MiltonUS Department of Agriculture, Agricultural Research Service, Forage and Range Research Laboratory, Utah State University, Logan, UT, USAThomas A. MonacoUniversity of California, Riverside, Riverside, CA, USAArlee M. MontalvoDepartment of Environment and Agronomy, National Institute for Agricultural and Food Research and Technology (INIA-CSIC), Madrid, SpainJose A. Navarro-CanoForest and Rangeland Stewardship Department, Colorado State University, Fort Collins, CO, USAMark W. PaschkeInstituto Nacional de Tecnología Agropecuaria (INTA), Universidad Nacional de la Patagonia Austral (UNPA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Santa Cruz, ArgentinaPablo Luis PeriUSDA – NRCS, Bozeman, MT, USAMonica L. PokornyUSDA Agricultural Research Service, Fort Keogh Livestock and Range Research Laboratory, Miles City, MT, USAMatthew J. RinellaPlant Science, Western Cape Department of Agriculture, Elsenburg, South AfricaNelmarie SaaymanRed Rock Resources LLC, Miles City, MT, USAMerilynn C. SchantzBush Heritage Australia, Eurardy, Western Australia, AustraliaTina ParkhurstDeptartment of Ecology, Evolution, and Behavior, University of Minnesota, St Paul, MN, USAEric W. SeabloomHolden Arboretum, Kirtland, OH, USAKatharine L. StubleDepartment of Natural Resources and Environmental Science, University of Nevada, Reno, NV, USAShauna M. UselmanDepartment of Wildland Resources & Ecology Center, Utah State University, Logan, UT, USAKari VeblenDepartment of Biology, University of Regina, Regina, Saskatchewan, CanadaScott WilsonCentre of eResearch and Digital Innovation, Federation University Australia, Ballarat, Victoria, AustraliaMegan WongSchool of Geography and Ocean Science, Nanjing University, Nanjing, ChinaZhiwei XuInstitute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO, USAKatharine L. Suding More

Tick paralysis is a common tick-borne illness in humans and animals throughout the world, caused by neurotoxins produced in the salivary glands of ticks and secreted into a host during the course of feeding by females and immature stages19. Fifty-nine ixodid and fourteen argasid ticks are currently believed to be involved in the transmission of tick paralysis worldwide19, 20. In Australia, I. holocyclus is considered to be the leading tick species implicated in the transmission of tick paralysis primarily in dogs, but also other species, viz. cats, sheep, cattle, goats, swine and horses. Humans are also occasionally affected, and the disease can be fatal2, 21. A second tick species, I. cornuatus has also been implicated in the transmission of tick paralysis in Australia; however, it is also considered a minor player in this disease22. Given the differences in their biology, distribution, and natural history of these two species, we focused on estimating the spatial distribution of I. holocyclus in the present study. We recognize, however, that it is important to consider the distributions of both species for proper epidemiological planning and management of tick paralysis in Australia.Ecological niche modeling is a well-tested approach for estimating species distributions based on abiotic factors13, 23. Several new recommendations have been made in recent years for proper construction of niche models; such as the appropriate thinning of occurrence data24, consideration of an accessible area for a species being studied (M)25, thorough exploration of model complexity26, 27, and use of multiple statistical criteria for model selection28, 29. We carefully considered all these recommendations to produce a robust spatial distribution model for I. holocyclus. The resulting replicated models were fairly consistent in predicting suitability for I. holocyclus, as indicated by moderate range estimates (Fig. 2B). Further, the MOP analysis indicated satisfactory performance of the present-day model with extrapolation only in small areas outside the predicted suitable areas. These qualities, along with the model’s very low omission rate (0.044%) gives high confidence in the predicted suitable area for this species in Australia. It will be essential, however, to confirm the actual presence of I. holocyclus outside the traditionally known areas through acarological surveys to assess our findings.The present-day spatial distribution predicted in this study (Fig. 2A) indicates that the geographic areas suitable for I. holocyclus match the currently known distribution of this species along the eastern seaboard, but the suitability also extends through most of the coastal areas in the south, and up to the Kimbolton Peninsula in Western Australia in the north. Highly suitable areas are present around and south of Perth, extending towards Albany, Western Australia. Most areas in Tasmania are also highly suitable for this species. The current distribution in the Eastern Seaboard may be wider than the traditionally known extents in some areas compared to Roberts30. It is likely that I. holocyclus will succeed in establishing permanent populations if introduced into areas that are currently free of them along the southern and northern coasts, and along the southwestern coast of Western Australia and Tasmania. Appropriate prevention of tick movement including pet inspections and quarantine will be necessary to avoid introductions.Future potential distribution of I. holocyclus in year 2050 based on both low- and high-emissions scenarios indicate moderate increases in climatic suitability from the present-day prediction (Fig. 4A,B); but noticeably also moderate to low loss of climatically suitable areas in 2050. This loss could be at least partly attributed to potential future temperature and precipitation conditions exceeding suitable ranges for these ticks in these areas, limiting their ability to survive. Predicted loss of suitable areas in future can also be observed to be irregular, and in some areas, particularly along northern Queensland and in Northern Territory, enveloped between stretches of suitable areas. Our use of relatively coarse resolution data (1 km2) limits our ability to thoroughly interpret such phenomenon, but this is likely due to variations in the geography in these areas that respond differently to future climate, as well as the potential increase in ocean temperature and subsequent influences on areas along the coast that may render them unsuitable for this species. Despite the noticeable loss in climatically suitable areas, likely no net loss in area will accrue for this species by 2050.Teo et al.31 assessed present and future potential distribution for I. holocyclus using both CLIMEX32, 33 and a novel, as-yet unpublished “climatic-range” approach to determine the suitability on monthly intervals. CLIMEX allows users to specify different upper and lower thresholds for climatic parameters, some of which were derived for their study from laboratory evaluations of I. holocyclus34. The present-day distribution reported in that study resembles our results in identification of a relatively narrow area along the East Coast as suitable; however, much of the northern and northeastern areas along the coast, the coasts of South Australia and southwestern Australia, and Tasmania are reported unsuitable. Their future predictions (2050) of the species’ potential distribution were based on two GCMs (CSIRO MK3 and MIROC-H) climate models, were also markedly different from our predictions, anticipating rather dramatic distributional loss for the species. Such model transfers are challenging, with many factors potentially producing inconsistencies35. However, the two studies reflect two fundamentally different classes of ecological niche models; CLIMEX is deterministic, whose predictions are largely constrained by user supplied threshold values for model inputs of physiological tolerance limits of a species33, whereas Maxent is a machine-learning correlative approach, in which known occurrences of a species is used in conjunction with environmental layers to determine conditions that meet a species’ environmental requirements, and therefore the suitability of geographic spaces. Although the former (CLIMEX) approach is appealing conceptually, scaling environmental dimensions between the micro-scales of physiological measurements and the macro-scales of geography is well-known to present practical and conceptual challenges36.Different ixodid ticks employ different life-history strategies in response to adverse environmental conditions, including behavioral adaptations, active uptake of atmospheric moisture, restriction of water-loss, and tolerance towards extreme temperatures37. Precisely which of these mechanisms I. holocyclus utilizes, if any at all, for its survival during diverse temperature and humidity conditions is not clearly known, but it is likely to involve multiple mechanisms. In this sense, the threshold values used by Teo et al.31, based purely on laboratory observations may have been overly restrictive, leading to a conservative distributional estimate for this species. Further, because relationships between abiotic variables and species’ occurrences are fairly complex and highly dimensional, a physiological thresholding approach wherein values are set independently for different abiotic parameters may not capture species’ relationships with environments adequately. The correlative approaches employed in the present study are data-driven, and as such may capture more of this complexity, with fewer problems of scaling across orders of magnitude of space and time.In conclusion, ticks are poikilothermic ectoparasites, whose survival, reproduction and other biological functions are regulated by ambient climatic conditions. Although ixodid ticks are known to regulate their body temperatures by moving about their habitat (vegetation), attempts to model their spatial distribution has resulted in models largely based on climate variables. Nevertheless, other factors such as host availability play a significant role in tick distribution, which unfortunately cannot be readily included in correlative ecological niche models largely because such data are rarely available. These suitability predictions, in addition to being entirely based on large-scale climate, also do not reveal the highly likely heterogeneity in abundance or density in different geographic areas within the realized climatically suitable areas. For these reasons, the distribution maps produced in this study must be used with some caution, and perhaps as a guide to target sampling and not as a substitute for thorough acarological surveys. More

Data constructionWe construct an annual panel dataset from 2000 to 2012 of 2549 coastal communities within 102 countries. Population counts from 2000 to 2012 for each community were calculated from the Landscan population database27 and coastal communities were defined as the lowest level administration units with an ocean coastline of each country using the Global Administrative Areas Database v2.7. Using the National Oceanic and Atmospheric Administration’s (NOAA) global nighttime lights data, we examine trends in economic activity before and after a cyclone event. The growth rate in average annual luminosity from nighttime lights trends with economic growth and has been used as an effective proxy for local economic activity22,24,28,29,30,31,32.However, trends in nighttime luminosity should not be interpreted as a measure of economic growth. Instead, we focus on tracking the dynamic impacts of nighttime luminosity (e.g. deviations from trends) that indicates whether an exposed community’s economic activity recovers or suffers permanent damage. The average elevation of each coastal community was calculated using a void-filled Shuttle Radar Topography Mission (SRTM) data at 3 arc-seconds, or approximately 90 m2 at the equator33. The SRTM has the potential to result in an overestimation of elevation in heavily built environment areas or areas of dense high forest canopies compared against locations without such trees. However, during the timeframe of our analysis, the SRTM product was the most appropriate and available product.The mangrove coverage dataset was adapted from the Continuous Global Mangrove Forest Cover for the 21st Century (CGMFC-21) database for the years 2000 to 201212. The coastline length of each community, based on Global Self-Consistent, Hierarchical, High-Resolution Shoreline Database v2.3.5 full resolution data34, was used to normalize the area of mangroves offshore of each coastal community creating a measurement for the “width” of mangroves per meter of coastline.Tropical storm locations for all years were recreated from the International Best Track Archive for Climate Stewardship (IBTrACS) Annual Tropical Cyclone Best Track Database35. Precise measurements of exposure, combined with high-resolution luminosity data, allows to distinguish the heterogeneous impacts of cyclones on exposed communities and the capacity for mangroves to shelter coastal economic activity at different elevations and for different mangrove widths. The intensity of exposure is measured by the distance of the cyclone’s “eye” from the exposed village’s nearest boundary.Tropical cyclone wind profile36, villages passing within 100 km of the cyclone’s eye were likely to experience maximum wind velocity and surface level pressure whereas those villages passing within more distant bands—i.e., 100–200 km and 200–300 km, were likely to experience similar surface level pressure but a non-linear reduction in wind velocity. Binning wind velocities in this way recognizes the highly non-linear relationship between wind velocity and on-the-ground damages from cyclone events37. We therefore expect the capacity for mangroves and elevation to shelter economic activity also to depend on this intensity of exposure.Our full sample encompasses nearly 400 million individuals in 102 countries and 2549 mangrove-holding communities (Table 1). Based on 2019 fiscal year World Bank categorizations, most of our sample resides in developing countries (85.1%) with 46.7% in lower-middle income (gross national income/per capita between $996 and $3895) and 35.3% in upper-middle income countries (gross national income/ per capita between $3896 and $12,056). We also find that most mangrove coverage in our sample exists within developing countries (88.7%) and overwhelmingly in upper-middle income countries (56.0%) in the Latin America and Caribbean (LAC) and East Asian and Pacific (EAP) developing regions. While only 14.9% of our sample’s global population resides in LAC countries, these countries account for 39.8% of mangrove holdings in our sample whereas the 45.5% of the population residing in EAP countries only account for 30.3% of mangrove coverage.Empirical strategyWe use a distributed-lag autoregressive model to measure the initial and permanent effect of cyclone exposure on economic activity in coastal communities. The growth in economic activity for each coastal community is proxied by the difference in logs between years, (growth={ln}left(luminosit{y}_{t}right)-{ln}left(luminosit{y}_{t-1}right)). Our estimating equation is$$growt{h}_{i,j,t}=sumlimits_{L=0}^{n}{[beta }_{L} x {C}_{i,j,t-L}]+{gamma }_{j}+{delta }_{t}+eta {X}_{i,j,t}+{epsilon }_{i,j,t}$$
(1)

where the (beta) coefficients capture the marginal effects, across three bins of cyclone exposure, on the growth rate of luminosity for the (j{^{prime}}th) administrative unit, within country (i), and in time (t-L) where (t) is the observed year and L is the number of lags ranging from (0 ; to ;n). Here, ({C}_{i,j,t}) is a vector of cyclone exposures binned by the distance from the cyclone’s “eye” to the nearest boundary of the exposed community ( More

Chemicals and reagentsThe fluopyram standard was purchased from the Environmental Protection Monitoring Institute of the Ministry of Agriculture of China at a concentration of 1000 mg/L. Analytical grade acetonitrile, acetone, dichloromethane, and sodium chloride were purchased from the Guangzhou Chemical Reagent Factory. Chromatographic grade Methanol and n-hexane were available from Thermo Fisher Scientific. Purified water was prepared using a Milli-Q reverse osmosis system (Millipore, Milford, MA, USA). Strata Florisil (FL-PR) 500 mg/6 mL SPE manufactured by Strata™ (5.0 mL n-hexane–acetone (9:1, V/V) solution pre-rinsing cartridge).A standard solution of 1000 μg/mL fluopyram was diluted in n-hexane, and the matrix extract of the blank sample was obtained by the extraction method. The matrix standard solutions of 0.025, 0.05, 0.10, 0.15 and 0.50 μg/mL were obtained by the step dilution. All prepared solutions were stored at temperature of 4 °C until further use.Soil sample collectionHainan latosol was collected from the Bailian banana experimental base in Chengmai (Hainan), Yunnan sandy soil was collected from Taoyuan banana experimental base in Longtou Street, Kunming (Yunnan) and Fujian plain alluvial soil was collected from the Zhangzhou banana experimental base (Fujian). 5–10 soil sampling points were randomly selected in each banana experimental base; the soil samples were collected from depths of 0–10 cm, and debris such as gravel, weeds, and plant roots were removed from each sample. The soil samples were obtained by the quarter method after mixing, dried, and stored after 20 mesh screening.Extraction and purification of flupyramSoil sample extraction was conducted as follows: in a 200 mL conical flask, 20.0 g of the drying soil sample and 40.0 mL acetonitrile was added. After shaking on a reciprocating shaker for 2 h, the mixture was filtered through filter paper. The filtrate was transferred to a stoppered measuring cylinder with 6.0 g NaCl. The stopper was inserted, and the mixture was vigorously shaken for 2 min. The mixture was left at 25 ± 2 °C for more than 30 min to separate the acetonitrile and aqueous solutions. Meanwhile, 10.0 mL of the supernatant were accurately transferred into a 100 mL round-bottom flask and concentrated by a rotatory evaporator at 40 °C to near dryness, which was dissolved in a 5.0 mL n-hexane–acetone (9:1, v/v) solution, vortexed, and mixed well for purification.Water sample extraction is shown below. A 20 mL water sample was transferred to a separatory funnel with 40.0 mL dichloromethane. After vigorously shaking it for 2 min and then letting it stand for 30 min, the lower layer solution was collected in a 100 mL round-bottom flask. The collected fluid was concentrated by a rotatory evaporator at 40 °C to near dryness and dissolved in 5.0 mL n-hexane–acetone (9:1, v/v) solution, vortexed, and mixed well for purification.Sample purification is described below. A 5.0 mL n-hexane–acetone (9:1, v/v) was used to preach the Strata Florisil (FL-PR) 500 mg/6 mL extraction column. When the leaching solvent level reached the surface of the column adsorption layer, the solution sample was immediately poured into the column be purified. Then, the purified solution was collected in a 100 mL round-bottom flask. A 5.0 mL n-hexane–acetone (9:1, v/v) solution was used to rinse the round-bottom flask residuum, after which the rinse solution was applied to elute the Florisil column. The rinsing and elution steps were repeated three times. The collected fluid was concentrated by a rotatory evaporator at 40 °C to near dryness and dissolved in 2.5 mL n-hexane for analysis.Instrumental conditionThe test was performed using the Theomer DSQII gas chromatography-mass spectrometer (GC–MS) with Xcalibur 2.0, software for data acquisition and analysis. A SLB-5MS analytical column (30 m × 0.25 mm × 0.25 μm) was used as chromatographic column. The injection volume was 1 μL without split injection, the carrier gas was helium (He, ≥ 99.999% purity), and the carrier gas flow rate was set to 1.0 mL/min. The protective gas was nitrogen (N2, ≥ 99.999% purity), and the injection port temperature was 250 °C. The chromatographic column temperature program was set as follows: the initial temperature at 80 °C was maintained for 1 min; then it was raised to 240 °C at a speed of 20 °C/min and maintained for 3 min; finally, the temperature was raised at a rate of 50 °C/min until 280 °C, where it was maintained for 7 min.The MS was operated in electron ionisation (EI) mode with an ionising energy of 70 eV. MS data were acquired in both full scan (m/z 50–500) mode for identification and selected ion monitoring (SIM) mode for quantification. The temperatures of the ion source and transfer line were 250 °C and 280 °C, respectively. The retention time of fluopyram was 10.59 min. The quantifier ions were m/z 223, and the qualifier ions were m/z 195 and m/z 173.Analytical method validationFirst, we addressed the linearity. The matrix standard of fluopyram was prepared in the range of 0.025–0.50 μg/mL and the determination was carried out, with the concentration of fluopyram matrix standard solution as the abscissa and the peak area obtained from the GC–MS as the ordinate. Linearity was calculated by plotting the relationship between the concentration and the peak area.The sensitivity analysis relied on the LOD and the limit of quantitation (LOQ). To evaluate the sensitivity of the method, they were obtained by adding the standard solution of fluopyram at the lowest concentration level in line with the requirements of the analytical method for blank samples. The LOD was the corresponding concentration when the signal-to-noise ratio (S/N) was 3, and S/N = 10 corresponds to the LOQ.Accuracy and precision were estimated as well. To determine the reliability of the method, fluopyram standard solutions with different concentrations were added to the blank sample for the recovery experiment. Fluopyram standard solutions with concentrations of 0.008, 0.600, and 1.000 mg/kg were added to the blank samples. This procedure was repeated five times for each concentration. The samples were subjected to extract, purify and analysis under the method the same conditions as described above. The recovery was calculated for the accuracy of the method, and the RSD was calculated for the precision.Soil dissipation experimentIn a number of 100 mL clean and sterilized conical flasks with covers, 20.0 g of soil was added (net weight converted by water content); then, 0.1 mL 1000 μg/mL fluopyram standard solution was pipetted into the conical flasks. Ultrapure water was added. The water was controlled to occupy 60% of the total volume. The flasks were shaken on a constant temperature oscillator for 2 min to mix the fluopyram evenly. Then, they were placed in an artificial climate incubator and exposed to light at 25 ± 2 °C for 12 h per day. According to the different soil types, they were divided into three treatment groups: Hainan, Yunnan, and Fujian. Each treatment group had three parallels and three blanks. The detection intervals were 2 h, 1, 3, 7, 14, 21, 28, 42 and 60 day, while the detection of fluopyram was performed based on the interval according to the shown methods. The dissipation kinetics of fluopyram in banana planting soil conformed to the first-order kinetic equation Ct = C0e−kt, where Ct is a pesticide concentration (mg/kg) at different times (day), C0 is an initial concentration (mg/kg), and k is the dissipation rate constant. The half-life of fluopyram is determined using Eq. (1).$$T_{1/2} = , ln 2/k$$
(1)
Soil adsorption experimentUsing the oscillation balance method, 5.0 g of soil was put into the 250 mL conical flasks with cover, which contained 25 mL fluopyram aqueous solutions with mass concentrations of 0.02, 0.1, 0.5, 2.5 and 4.0 mg/L (containing 0.01 mol/L CaCl2), respectively. The soils were divided into three treatment groups: Hainan, Yunnan, and Fujian (based on the different soil types). The fluopyram aqueous solution and the blank soil aqueous solution (both containing 0.01 mol/L CaCl2) were used as controls. Each treatment group had three replicates. The conical flasks were then placed in a constant temperature oscillator at 25 ± 2 °C for 24 h to prepare the suspension. The suspension was transferred to a centrifuge tube for high-speed centrifugation, and 80% of the total volume of the supernatant was used for determination. The fluopyram in the supernatant was extracted and determined under the methods as described above, and the Freundlich equation model (see Eq. 2) was used to describe the adsorption law for fluopyram in soil.$${text{Freundlich: }}C_{s} = K_{f} times C_{e}^{1/n}$$
(2)
where Cs is adsorption content of pesticide in soil (mg/kg), Ce is concentration of the pesticide in aqueous solution at adsorption equilibrium (mg/L), Kf is the soil adsorption coefficient of the Freundlich model (L/kg), indicating the pesticide adsorption capacity of the soil and 1/n is a slope rate of the curve between Cs and Ce, reflecting the heterogeneity of the adsorbent surface.The relationship between the adsorption free energy of soil to pesticides (ΔG, kJ/mol) and the soil adsorption coefficient Koc is expressed using Eq. (3).$$Delta G , = – RTln K_{oc}$$
(3)
where Koc is the soil adsorption coefficient (Koc = Kf/OC × 100) expressed by organic carbon content (L/kg), OC is soil organic carbon content (%), R is the molar gas constant (J/K mol), and T is absolute temperature (K).Soil leaching experimentA plexiglass tube with an inner diameter of 5 cm and a length of 40 cm was used as a packed column. A layer of cotton, a 1 cm thick quartz sand layer, and a layer of filter paper were added at the bottom of the column. Dry soil (700–800.0 g) was weighed for filling, and the column was fully wetted with ultrapure water to prepare a 30 ± 0.2 cm high leaching soil column. 0.1 mL of 1000 μg/mL fluopyram solution was further added to 5.0 g of soil. After the solution completely volatilized, it was evenly spread on the top of the soil column, and a layer of filter paper and a layer of 1 cm thick quartz sand were added to the top of the soil. During the test, ultrapure water was used for washing the soil column for 10 h at a speed of 30 mL/h, and the leaching solution was collected. After washing, the soil column was removed and was cut into four sections of 1–5, 5–10, 10–20 and 20–30 cm. The residues of fluopyram in the soil samples and leaching solutions were extracted and determined under the methods as described above. According to the three soil types, they were divided into Hainan, Yunnan and Fujian treatment groups, where each group received another parallel treatment. More

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