Ecology

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Due to contrasts in oceanographic properties along the NAP24, the sampling grid was split in two subregions: north and south (Fig. 1; see “Methods”). The north and south subregions showed from the satellite data a much higher spring/summer (November–February) mean chlorophyll-a (Chl-a) in 2015/2016 than the decadal average time series (2010–2019; Table 1). In agreement with the El Niño effects10,16, the sea surface temperature (SST) and the air temperature showed substantially lower mean values during the spring/summer of 2015/2016 along the subregions (Table 1). However, there was an evident spatial/temporal variability in sea ice concentration/duration between the subregions, with a northward (southward) lower (higher) mean value during 2015/2016 in relation to the decadal average (Table 1). Along the south subregion during the spring/summer of 2015/2016, the increased Chl-a during January followed the decline in the sea ice concentration over the spring and early summer, concurrent with increased SST, which was markedly colder throughout the seasonal phytoplankton succession (Fig. 2a). These results to the south subregion are consistent with previous studies along the WAP, in which years characterized by longer sea ice cover in winter have led to higher phytoplankton biomass in the following summer associated with a more stable water column11,16,26. To the north subregion, however, although there was a similar pattern between Chl-a and SST, the increased Chl-a during January was not related with the sea ice retreat (Fig. 2b). Moreover, there was a clear difference between the Chl-a peaks (the highest Chl-a value reached) along the subregions from the satellite data. The Chl-a peak in the south subregion occurred in early January (10 January 2016, reaching 1.73 mg m–3), whereas in the north subregion the Chl-a peak was observed in late January (29 January 2016, reaching 2.23 mg m–3).Fig. 1: Study area.Location of hydrographic stations is marked by open circles (November), stars (January), and blue circles (February). The black dashed lines indicate the subregions (north and south) along the NAP and delimit the areas used to estimate average remote sensing measurements. The decadal-mean (2010–2019) remote sensing chlorophyll-a (Chl-a) is exhibited in the background, indicating the biomass (Chl-a) distribution of phytoplankton along the NAP in the last decade. An inset map in the lower right corner shows the location of the NAP within the Atlantic sector of the Southern Ocean.Full size imageTable 1 Biological production and ocean/atmosphere parameters by measurements of remote sensing and local meteorological stations during spring/summer in the NAP subregions.Full size tableFig. 2: Biological production and sea ice dynamics in the NAP seasonal phytoplankton succession of 2015/2016.Continuum remote sensing measurements of chlorophyll-a (Chl-a; solid green line), sea surface temperature (SST; solid blue line), and sea ice concentration (gray area) along the NAP, in south (a) and north (b) subregions during spring/summer of 2015/2016. The dashed green, blue and gray lines indicate the decadal average (2010–2019) of Chl-a, SST, and sea ice concentration, respectively. The solid light green lines represent the Chl-a interpolated values. The background shades show the in situ data sampling periods. It is important to note that Chl-a remote sensing data in Antarctic coastal waters are typically underestimated in respect to in situ Chl-a data (see Supplementary Fig. 1)12,29.Full size imageIt has been estimated that drifters entrained in the Gerlache Strait Current and the Bransfield Strait Current exit the Bransfield Strait in 10–20 days17, which is consistent with the interval of 19 days between both Chl-a peaks when considering the extreme distance between the subregions (see Fig. 1). These authors also estimated that drifters deployed in the Gerlache Strait Current were quickly advected out of the Gerlache Strait in less than 1 week (i.e., low residence time)17, which supports the similar diatom species assemblages identified in our microscopic analysis between stations of the Gerlache Strait and southwestern Bransfield Strait24. Therefore, it is plausible that phytoplankton growth in the north of the Gerlache Strait may be laterally advected northward into the Bransfield Strait, explaining the observed concomitant increase of satellite Chl-a data in both subregions from spring, associated with sea ice retreat southward (Fig. 2). In addition, as phytoplankton biomass tends to accumulate northward17,27,28, the advection processes could also explain the temporal and intensity differences of the Chl-a peaks along the subregions (see Fig. 2). This suggests that there was a link between the sea ice dynamics, phytoplankton biomass (Chl-a) and advection processes along the NAP during the spring/summer of 2015/2016, in which the sea ice melting first triggered an increase in phytoplankton biomass through water column stratification along the south subregion, and the advection processes led to a subsequent increase northward.The satellite Chl-a data require extensive validation with in situ data, especially in polar regions, where cloud cover is ubiquitous and performance is typically poor, due to not properly accurate Chl-a algorithms12,29. For that, although the mean Chl-a in 2015/2016 from the satellite data was approximately twice as large as the decadal average, there was a severe discrepancy in the mean Chl-a values observed between the in situ and remote sensing data (see Table 1 and Supplementary Table 1). This highlights the importance of the in situ dataset reported here, especially evident during February 2016, when the signal of an intense diatom bloom ( > 40 mg m–3 Chl-a)24 was not captured in the satellite data (Supplementary Fig. 1), supporting that phytoplankton biomass accumulation during this summer was much higher than recorded by remote sensing observations (see Table 1). In general, the in situ Chl-a achieved its maximum (40 mg m–3) and higher mean value (17.4 mg m–3) during February comparing to November and January (Supplementary Table 1).Phytoplankton community structure during the spring/summer of 2015/2016 was assessed through Chemical taxonomy (CHEMTAX) software, using accessory pigments versus in situ Chl-a concentrations measured via high-performance liquid chromatography (HPLC; see “Methods”). The main phytoplankton group over the season were diatoms, followed by haptophytes (Phaeocystis antarctica), cryptophytes, and dinoflagellates, according to the succession stage (Fig. 3a). Diatoms dominated the phytoplankton community composition in relation to the other groups along the whole in situ sampling period, although their relative biomass (to the total in situ Chl-a) was lower in some stations compared to others in different moments during spring/summer (Fig. 3a). To assess the degree to which the water column structure was a primary driver for development and intensity of diatom growth3,24, the mixed layer depth (MLD) and water column stability were calculated as a function of seawater potential density (see “Methods”). There was an inverse polynomial relationship between MLD and mean upper ocean stability (averaged over 5−150 m depth; hereafter referred to as upper ocean stability) (Fig. 3b). The significant positive exponential relationship between the upper ocean stability and diatom absolute concentrations (in situ Chl-a) demonstrates that stability, associated with MLD, was an important driver of diatom dynamics (Fig. 3b). This elucidates the increase in biological production during summer months of 2016, when upper ocean physical structures (MLD and stability) were sufficiently shallow and stable to produce the high phytoplankton biomass (in situ Chl-a) registered here. However, as MLD and stability showed similar values between summer months (Supplementary Table 1), only the upper ocean physical structures cannot be accounted for the high differences of in situ Chl-a values observed between diatom blooms in January (maximum of 12 mg m–3) and February (maximum of 40 mg m–3). Likewise, also not explaining these differences of in situ Chl-a values between summer months, macronutrients were highly abundant throughout the seasonal phytoplankton succession (Supplementary Table 1). Furthermore, although no measurements of dissolved iron, which can be considered as a limiting factor to primary productivity30, were carried out here, the Antarctic Peninsula continental shelves have been depicted as a substantial source of this micronutrient to the upper ocean, not limiting phytoplankton growth even during intense blooms31,32.Fig. 3: Phytoplankton community composition and upper ocean physical structures along the NAP seasonal phytoplankton succession of 2015/2016.a Relative biomass (to the total in situ chlorophyll-a; Chl-a) distribution of phytoplankton groups on surface, via HPLC/CHEMTAX analysis, during spring/summer of 2015/2016 along the NAP subregions. The black open circles indicate diatoms, the blue squares indicate Phaeocystis antarctica, the gray diamonds with crosses indicate cryptophytes, the green triangles indicate dinoflagellates, and the light gray open circles indicate green flagellates. b Exponential curve (R2 = 0.57; p 40% the community composition proportion in respect to the total Chl-a (considering the three fractionated size classes). Symbol color indicates the sampling month in respect to November (brown), January (gray), and February (black). The inset shows the polynomial inverse relationship (R2 = 0.51; p  70 µm in length; ref. 24), during January a large number ( > 2.5 × 106 cells L–1) of small ( More

Data sourcesEighteen rice-producing countries were selected for our analysis (Supplementary Table 1). Those countries account for 88 and 86% of global rice production and harvested rice area2, respectively (FAOSTAT, 2015–2017). We followed two steps to select the dominant cropping systems in each country. Within each country, our study focused on the main rice-producing area(s) (Supplementary Tables 2 and 3). For example, in the case of Brazil, we selected the southern and northern regions, which together account for nearly all rice production in this country. In the case of Vietnam, we selected the Mekong Delta region, which accounts for nearly 60% of national rice production57. While we tried to cover all major rice cropping systems in each country, this was not possible in the case of rainfed lowland rice cropping systems in northeastern Thailand and eastern India because of lack of reliable estimates of yield potential and access to farmer yield and management data. Once the main rice-producing region(s) in each country was (were) identified, we then determined the dominant rice cropping system(s) for each of them (Supplementary Table 3). We note that “cropping system” refers to a unique combination of a number of rice crops planted on the same piece of land within a 12-month period (and their temporal arrangement), water regime (rainfed or irrigated), and ecosystem (upland or lowland) (Supplementary Fig. 1 and Supplementary Table 2). In our study, rice cropping systems are single-, double-, or triple-season rice; none of the cropping systems are ratoon rice. Following the previous examples, two cropping systems were selected for Brazil (rainfed upland single rice and lowland irrigated single rice in the northern and southern regions, respectively) and two systems (double and triple) were selected for the Mekong Delta region in Vietnam. These systems account for nearly all rice harvested areas in these regions. We distinguished between rice-based cropping systems sowing hybrid versus inbred cultivars in the southern USA. Across the 18 countries, this study included a total of 32 rice cropping systems, which, in turn, covered 51% of the global rice harvested area (Supplementary Tables 1 and 3). Note that the area coverage reported here corresponds to that accounted by 32 cropping systems (and not by the countries where the cropping systems were located). These systems portrayed a wide range of biophysical and socio-economic backgrounds (Supplementary Figs. 1 and 2 and Supplementary Tables 1 and 2), leading to average rice yields ranging from 2–10.4 Mg ha−1 (Supplementary Fig. 3). For data analysis purposes, rice cropping systems were classified into tropical and non-tropical9,58,59 and also based upon water regime and crop season.Agronomic information was collected via structure questionnaires completed by agricultural specialists in each country or region (Supplementary Table 6). The collected data included field size, tillage method, crop establishment method, degree of mechanization for each field operation, seeding rate, crop establishment, and harvest dates, nutrient fertilizer rates, manure type, and rate, pesticides (number of applications, products, and rates), irrigation amount (in irrigated systems), energy source for irrigation pumping, labor input, and straw management (Supplementary Tables 4 and 5). Average values for each cropping system reported by country experts were retrieved from survey data available from previous projects (Supplementary Table 7). Rice grain yield was reported at a standard moisture content of 140 g H2O kg−1 grain, separately for each crop cycle, using data from, at least, three recent cropping seasons in each cropping system. In the case of irrigated rice cropping system in Nigeria and Mali, data were only available for one crop cycle in double-season rice. In this case, we assumed management and actual yield to be identical in the two crop cycles.In all cases, and wherever possible, data were cross-validated with other independent datasets (e.g., FAOSTAT, World Bank, IFA, and published journal papers), which gives confidence about the representativeness and accuracy of the survey data. For example, we estimated area-weighted national yield according to actual yield provided for each cropping system and annual rice harvested area in each system for each of the 18 countries. Comparison of these yields against those reported by FAOSTAT2 showed a strong association and agreement between data sources (Supplementary Fig. 10). We also cross-validated actual yield, N fertilizer, labor, and irrigation from our database with those reported by previous studies (published after the year 2000) based on on-farm data collected in ten selected countries. Due to the lack of on-farm data on irrigation, we used published data collected from experiments that follow typical farmer irrigation practices. In the case of irrigation, our cross-validation differentiated between crop seasons (wet versus dry) in the case of irrigated double-season rice cropping systems. In all cases, average yield, N fertilizer, labor, and irrigation from our database fell within (or very close) the range of values reported in previously published studies for those same cropping systems (Supplementary Table 8). Measured daily weather data, including daily solar radiation, minimum and maximum temperatures, and precipitation, were derived from representative weather stations in each region (Supplementary Fig. 2 and Supplementary Table 9). Data on per-capita gross domestic product (GDP) during 2015–2017 were retrieved for each country to explore relationships between yield gap and economic development60 (Supplementary Fig. 9 and Supplementary Table 1).Estimation of yield gapsThe yield gap is defined as the difference between yield potential and average farmer yield. Estimates of yield potential for irrigated rice or water-limited yield potential for rainfed rice were adopted from Global Yield Gap Atlas (GYGA)61 (Supplementary Table 7). Yield potential simulation in GYGA was performed using crop growth and development model ORYZA2000 or ORYZA (v3) (except for APSIM in the case of India) and based on actual data on crop management, soil data, measured daily weather data, and representative rice varieties planted in each region (see details for yield potential simulation in Supplementary Information Text Section 1). Data on yield potential were not available for Australia (AUIS) in GYGA; hence, we used estimates of yield potential from Lacy et al.62. Yield potential (or water-limited yield potential for rainfed rice) and average yields were computed separately for each rice crop in each rice cropping system (Supplementary Fig. 3). The coefficient of variation (CV) of yield potential (or water-limited yield potential) was estimated for each cropping system (Supplementary Fig. 4). In this study, average rice yield was expressed as percentage of the yield potential (or water-limited yield potential for rainfed rice) for each cropping system (Fig. 1 and Supplementary Fig. 5). In those cropping systems where more than one rice crop is grown within a 12-month period, we estimated yield potential and average yield on both per-crop and annual basis by averaging and summing up the estimates for each rice crop, respectively. In the case of per-crop averages, for those cropping systems in which the harvested rice area changed between crop cycles, we weighted the values for each cycles based on the associated harvested rice area. However, for simplicity, the main text reports only the values on a per-crop basis; annual estimates are provided in the Supplementary Information. Normalizing average yield by the yield potential at each site provides a direct comparison of yield gap closure across systems with diverse biophysical backgrounds (e.g., variation in solar radiation, temperature, and water supply). Without this normalization, one might make biased assessment in relation to the available room for improving yield. For example, an actual yield of 8 Mg ha−1 is equivalent to 80% of yield potential in the cropping system of central China, whereas a yield of 8 Mg ha−1 achieved by irrigated rice farmers in Brazil only represents 55% of yield potential (Supplementary Fig. 3).Quantifying resource-use efficiencyWe assessed the performance of rice production by calculating the following metrics: global warming potential (GWP), fossil-fuel energy inputs, water supply (irrigation plus in-season precipitation), number of pesticide applications, nitrogen (N) balance, and labor input, each expressed on an area and yield-scaled basis (Figs. 2, 3 and 4 and Supplementary Figs. 6, 7 and 11). We estimated metrics on both per-crop and annual basis and report the values on a per-crop basis in the main text while the annual estimates are provided in the Supplementary Information. In the case of GWP, it includes CO2, CH4, and N2O emissions (expressed as CO2-eq) from (i) production, packaging, and transportation of agricultural inputs (seed, fertilizer, pesticides, machinery, etc.), (ii) fossil-fuel energy directly used for farm operations (including irrigation pumping), and (iii) CH4 and N2O emission during rice cultivation63. Emissions from agricultural inputs were calculated on application rates and associated GHG emissions factors (see details in Supplementary Information Text Section 2, Supplementary Table 10). In the case of fossil fuel used for field operations, it was calculated based on the number and type of farm operations and associated fuel requirements (Supplementary Table 11). Total N2O emissions were calculated as the sum of direct and indirect N2O emissions. A previous meta-analysis including rice showed that direct soil N2O emissions can be estimated from the magnitude of N-surplus, which was calculated as applied N inputs minus accumulated N in aboveground biomass at physiological maturity21. Therefore, direct soil N2O emissions for a given rice crop cycle were estimated following van Groenigen et al. N-balance approach21. Indirect N2O emissions were estimated based on the Intergovernmental Panel on Climate Change (IPCC) methodology64, assuming indirect N2O emissions represent 20% of direct N2O emissions. The CH4 emissions from rice paddy field were calculated following IPCC65. Following this approach, CH4 emissions are estimated considering the duration of the rice cultivation period, water regime during the cultivation period and during the pre-season before the cultivation period, and type and amount of organic amendment applied (e.g., straw, manure, compost) based on a baseline emission factor. We assumed no net change in soil carbon stocks as soil organic matter is typically at steady state in lowland rice66. We did not attempt to estimate changes on soil C in the upland rice system in Brazil. All emissions were converted to CO2-eq, with GWP for CH4 set at 25 relatives to CO2 and 298 for N2O on a per mass basis over a 100-year time horizon67. For each rice crop cycle in each of the 32 rice systems, GWP was calculated as the sum of CO2, CH4, and N2O emissions expressed as CO2-eq. (Details on N2O and CH4 emissions estimates and GWP calculations are provided in Supplementary Information Text Section 2).Calculation of energy inputs was similar to that of GWP and was based on the reported rates of agricultural inputs and field operations and associated embodied energy (see details for energy input estimates in Supplementary Information Text Section 2, Supplementary Table 12). Human labor was also included in the calculation of energy inputs. There was a strong positive relationship between energy input and GWP on both per-crop (r = 0.81; p  More

Bed bugsFour bed bug populations (one laboratory strain and three collected from infested homes) were used in this study (Table 1). All populations were reared in the laboratory as described by DeVries et al.28. Briefly, bed bugs were maintained in 168 cm3 plastic containers on paper substrate at 25 °C, 50% relative humidity, and a photoperiod of 12 h:12 h (Light:Dark). Bed bugs were fed defibrinated rabbit blood (Hemostat Laboratories, Dixon, CA, USA) weekly using an artificial feeding system. This system maintained blood at 35 °C by circulating water through custom-made water-jacketed glass feeders. An artificial membrane (plant budding tape, A.M. Leonared, Piqua, OH, USA) was stretched over the bottom of each glass feeder, containing the blood while simultaneously allowing bed bugs to feed through it. In all experiments, adult males starved for 7–10 days were used. All populations were used for documenting responses to human skin swabs. The WS population was used for bioassays with various human volunteers and hexane extracted swabs, and the JC population was used for testing various lipids.Table 1 Bed bug populations used in this study.Full size tableSkin swab collectionThe North Carolina State University Institutional Review Board approved this study (IRB #14173). Informed consent was obtained from all human participants, and all the methods were performed according to the relevant guidelines and regulations. Six human volunteers (3 males, 3 females) ranging from 25 to 50 years old representing several ethnicities (white/Caucasian, Hispanic, Asian) provided samples for this project. Skin swabs were collected following the exact methods outlined by DeVries et al.16. In our 2019 study, these swabs were reported to attract bed bugs independent of other cues in Y-tube olfactometer assays. Briefly, participants were asked to follow a standard operating procedure, which was reviewed with them prior to sample collection. Before collecting skin swabs, participants were asked to not to eat ‘spicy’ food for at least 24 h, take a morning shower, avoid the use of deodorant and cosmetics after showering, and avoid strenuous physical activity. Skin swabs were collected 4–8 h after showering. Hands were washed with water only before lifting filter paper. Swabs were collected using 4.5 cm diameter filter paper discs (#1; Whatman plc, Madistone, United Kingdom). Both sides of a single filter paper disc were rubbed over the left arm from hand to armpit for 12 s, left leg from lower thigh to ankle for 12 s, and left armpit for 6 s. This procedure was repeated on the right side using a new filter paper disc, so that two samples were collected during each swabbing session. The skin swab samples were then stored in glass vials at − 20 °C, and used within one month of collection. The swabs from all human volunteers were used to compare participants and establish that bed bugs responded similarly to all, and participant A’s skin swabs were used for all subsequent bioassays.Two-choice arrestment bioassaysTwo-choice bioassays were conducted in plastic Petri dishes of 6 cm diameter (Corning Life Sciences, Durham, NC, USA) (Fig. 1). The bottom surface of each Petri dish was roughened so that bed bugs could freely move about the arena. Two tents (3 × 1.5 cm) were created using filter paper (Whatman #1). One tent served as the control tent, and the other served as the treatment tent. Control tents were either untreated (nothing added) or treated with hexane only. Treatment tents were either made directly from human odor swabs, treated with human odor extract (in hexane), or treated with a specific compound (in hexane). Tents were allowed 60 min to acclimate to room conditions and allow for the solvent to evaporate prior to initiating bioassays. The positions of tents (treatment and control) were alternated to account for any side-bias.Figure 1Two-choice behavioral assay (top-view) consisting of two equal size paper shelter tents. A clean filter paper (control) was always paired with a treated filter paper that either represented a human skin swab, hexane extract of swabbed paper, SPE fraction of human skin swab extract, or authentic TAGs. A single male bed bug was introduced into the center of each arena and allowed to select a tent to arrest under.Full size imageAdult male bed bugs were housed in individual vials for 24 h prior to each experiment. A single adult male bed bug was released in the middle of the arena 5 h into the scotophase, by transferring it on its harborage. The harborage material was removed immediately after the bed bug moved off of it (the harborage). Bed bugs were allowed the remaining 7 h of the scotophase to freely move around the arena, with their final position reported 3 h into the photophase. Bed bugs that were in contact with the filter paper with any part of their body were recorded as making a choice (i.e. arrestment state); others not in contact with either filter paper tent were recorded as non-responders, reported in the figures, but not used in data analysis. It should be noted that momentary pauses in movement (feeding or other behaviors) are not referred to as arrestment in this study. In total, 15–39 replicates were performed for each experiment (reported for each bioassay).Bioassays with human skin swabsBioassays with human skin swabs were performed to understand if bed bug arrestment behavior (1) differed among different bed bug populations, and (2) influenced by different host odors. Skin swabs were removed from the freezer, equilibrated to room temperature, divided into three equal parts and trimmed to a rectangular shape corresponding to the size of a shelter tent (Fig. 1). Skin swabs from participant A were used to evaluate the responses of four bed bug populations (Table 1). Skin swabs from all participants A–F were used to evaluate the robustness of our findings across multiple human hosts.Skin swab extraction and fractionationSkin swabs collected from volunteer A were pooled and extracted in hexane. Extraction procedures were carried out sequentially by placing a single skin swab into a 20 ml glass vial containing 5 ml of hexane, vortexing for 30 s, then moving the filter paper to a new 20 ml vial containing 5 ml of hexane and repeating the process. Three sequential extractions were performed for each skin swab, and a minimum of 10 skin swabs (collected over several days) were used for each extraction. After all skin swabs were extracted, all sequential hexane extracts were combined and concentrated to a final concentration of one skin swab equivalent per 300 µl, or one bioassay equivalent (BE) per 100 µl (since each swab was used for 3 bioassays; see “Bioassays with human skin swabs” for more information on the size used for each bioassay). Control swabs were also extracted. These swabs were treated identically to the skin swabs, except they did not contact human skin.To determine what compound classes were responsible for the observed behavior, hexane extracts were fractionated using solid phase extraction (SPE). Extracted samples were concentrated to 1 BE/10 µl hexane, then loaded onto a 1 g silica SPE column (6 ml total volume; J.T. Baker, Phillipsburg, NJ, USA). The column was eluted with the following solvents (4 ml of each, each repeated twice sequentially): hexane, 2% ether (in hexane), 5% ether (in hexane), 10% ether (in hexane), 20% ether (in hexane), 50% ether (in hexane), 100% ether, ethyl acetate, and methanol (all solvents acquired from Sigma Aldrich, St. Louis, MO, USA). Each solvent fraction was then concentrated to a final concentration of 1 BE/100 µl and stored at − 20 °C.Bioassays with extracted and fractionated human skin swabsFor all extraction and fractionation bioassays, filter paper tents were cut to a size of 3 cm × 1.5 cm (Fig. 1) and treated with 100 µl (1 BE) of extracted or fractionated human skin swabs (50 µl on each side). A dose–response bioassay was run first to determine if the compounds responsible for bed bug arrestment responses could be extracted and at what concentration (BE) they were behaviorally active. Dilutions were made in hexane, with control tents receiving extracts of control filter paper. At least 20 replicates were conducted for each concentration. After validating an appropriate BE that could be used in future experiments, SPE fractions were diluted in hexane to 0.1 BE and applied to filter paper tents as previously described (50 µl per side). A minimum of 15 replicates were conducted for each fraction to identify behaviorally active fractions.Compound identificationTo better understand what classes of compounds were present in behaviorally active fractions, we conducted thin layer chromatography (TLC) with known standards. A flexible, silica (250 µm) TLC plate (Whatman) was placed into a glass chamber containing a solvent layer of 1.5 cm. The plate was cleaned twice with acetone, then standards (triglyceride [TAG], wax ester, squalene) and samples (fractions) were each loaded into separate lanes. The plate was developed twice in 10% ether (in hexane), then visualized non-destructively with iodine.In addition, behaviorally active fractions were further evaluated for their composition with GC–MS and LC–MS. GC–MS was employed to analyze free fatty acids, squalene, and cholesterol29, whereas LC–MS was employed to characterize the intact skin lipids as previously described30. Samples were analyzed with a GC 7890A coupled to the MS 5975 VL analyzer (Agilent Technologies, CA, USA) following derivatization. Briefly, 50 µL of the extract dissolved in isopropanol were dried under nitrogen and derivatized with 100 µL BSTFA containing 1% trimethylchlorosilane (TCMS) in pyridine to generate the trimethylsilyl (TMS) derivatives at 60 °C for 60 min. GC separation was performed with a 30 m × 0.250 mm (i.d.) × 0.25 µm film thickness DB-5MS fused silica column (Agilent). Helium was used as the carrier gas. Samples were acquired in scan mode by means of electron impact (EI) MS.Liquid-chromatography coupled to the MS analyzer by means of an electrospray interface (ESI) was used to determine abundance and ESI tandem MS of non-volatile lipids as previously described29,30. LC separation was performed with a reverse phase Zorbax SB-C8 column (2.1 × 100 mm, 1.8 μm particle size, Agilent). Data were acquired in the positive ion mode at unit mass resolving power by scanning ions between m/z 100 and 1000 with G6410A series triple quadrupole (QqQ) (Agilent). LC runs and MS spectra were processed with the Mass Hunter software (B.09.00 version).Bioassays with triglyceridesAfter determining that TAGs were prominent compounds in bioactive skin swab fractions, commercially available TAGs were evaluated for behavioral activity. Filter paper tents were treated with 100 µl of hexane (50 µl to each side) containing TAG standards. First, tripalmitin (16:0/16:0/16:0) (Sigma-Aldrich) was evaluated in a dose–response fashion (60 µg to 0.6 µg) to determine what level of TAG was appropriate for bioassays. The upper level of testing was set at 60 µg as a conservative estimate of the amount of TAGs bed bugs may be exposed to, based on calculations of our arena size and previous reports of TAGs on human skin and sebum. Specifically, previous reports documented that 1.5 mg of sebum could be passively collected using Sebutape from an area of 4.7 cm230,31. Because TAGs typically constitute 60% of human sebum32, it is reasonable to assume that passive collection of sebum can result in  > 190 µg/cm2 of TAGs in a short amount of time (30 min). Our sampling methods involved swabbing rather than passive collection, but our use of 60 µg over a 9 cm2 (two sides of 4.5 cm2) shelter tent (6.67 µg/cm2) is a low-estimate of the amount of TAGs collected (although this was not directly measured in the current study). Other TAGs that we tested at a concentration of 60 µg per 9 cm2 included the saturated TAGs trimyristin (14:0/14:0/14:0) and tristearin (18:0/18:0/18:0) and the unsaturated TAGs triolein (18:1/18:1/18:1), trilinolein (18:2/18:2/18:2), and trilinolenin (18:3/18:3/18:3) (all from Sigma-Aldrich). A minimum of 30 replicates were conducted with each TAG.Statistical analysisA Chi-square goodness of fit test was used to compare the responses of bed bugs to control versus treated tents in all two-choice bioassays, with the null hypothesis that if bed bugs do not respond differentially to treated tents they should display a 1:1 preference ratio for both sides of the assay. All tests were conducted in SPSS Version 26 (IBM Corp., Armonk, NY). More

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Site characteristics and experimental designIn this study, agricultural soils with five SOM contents were collected in 2015 from the following three different locations with the same climate type (the moderate temperate continental climate) in Northeast China (Table S3 and Fig. 1): Bei’an (BA), Hailun (HL), and Dehui (DH). Their MAT and MAP range from 1.0 to 4.4 and 520 to 550, respectively. After collection, the samples were transported to the Hailun Agricultural Ecological Experimental Station (HL), where the samples were packed into the same PVC tubes. Moving the soil from these three initial sampling points to the HL may have had some influence on the microbes, but compared with longer-distance soil translocation across different climatic zones, the HL site can be regarded as an in situ site that reflects the original climatic conditions. The SOM contents were 2%, 3%, 5%, 7%, and 9% (equivalent to 10, 18, 28, 36, and 56 g C kg−1 soil−1, respectively), and all the soils were classified as Mollisols according to the FAO classification. Here, we designed a unique latitudinal soil translocation experiment to investigate the relationship between the bacterial and fungal community stability and the responses of soil C molecular structure to climate warming. The detailed protocol for the experiment was the following: (1) Forty kilograms of topsoil (0–25 cm) was collected for each SOM. The latitude and longitude of the sampling sites and soil geochemical characteristics are shown in Tables S3 and S4. Detailed data can be found in Supplementary Data 1. (2) The soil was homogenized using a 2 mm sieve and filled with sterilized PVC tubes. The PVC tube was 5 cm in diameter at the bottom and 31 cm in height. Each tube was filled with a 25 cm-high soil column, which corresponded to approximately 1 kg of soil. The bottom of the pipe was filled with 1 cm quartz sand, and a 5 cm space was left at the top. (3) From October to November 2015, 90 PVC pipes containing soil (5 SOM gradients × 3 replicates × 6 climatic conditions) were transported to six ecological research stations with different geoclimatic conditions and SOM contents, and 15 PVC pipes were placed in each station. Once the experiment was set up, the weeds growing in each PVC pipe were manually removed every 2–3 weeks to avoid the impact of plants.The six ecological research stations were the Hailun Agricultural Ecological Experimental Station (HL, N 47°27′, E 126°55′) in Heilongjiang Province, Shenyang Agriculture Ecological Experimental Station (SY, N 41°49′, E 123°33′) in Liaoning Province, Fengqiu Agricultural Ecological Experimental Station (FQ, N 35°03′, E 114°23′) in Henan Province, Changshu Agricultural Ecological Experimental Station (CS, N 31°41′, E 120°41′) in Jiangsu Province, Yingtan Red Soil Ecological Experiment Station (YT, N 28°12′, E 116°55′) in Jiangxi Province and Guangzhou National Agricultural Science and Technology Park (GZ, N 23°23′, E 113°27′) in Guangdong Province. The MAT and MAP at the six ecological research stations ranged from 1.5 to 21.9 °C and from 550 to 1750 mm from north to south, respectively. Details of their climatic conditions (e.g., climatic types) are shown in Table S5. All tubes were removed from each station after 1 year.The soil samples were stored on dry ice and rapidly transported back to the laboratory. The soil pH was measured by the potentiometric method. Nitrate (NO3−-N) and ammonium nitrogen (NH4+-N) were measured by the Kjeldahl method. DOC was measured using a total organic carbon analyzer (Shimadzu Corporation, Kyoto, Japan). SOC was determined by wet digestion using the potassium dichromate method53. Microbial biomass C (MBC) was measured by the chloroform fumigation-incubation method54. All geochemical attributes are shown in Table S4.Solid-state 13C NMR analysis of soil C molecular groupsSolid-state 13C NMR spectroscopy analysis was performed to determine the molecular structure of SOC. A Bruker-Avance-iii-300 spectrometer was used at a frequency of 75 MHz (300 MHz 1H). Before the examination, the soil samples were pretreated with hydrofluoric acid to eliminate the interference of Fe3+ and Mn2+ ions in the soil. Specifically, 5 g of air-dried soil was weighed in a 100 ml centrifuge tube with 50 ml of hydrofluoric acid solution (10% v/v) and shaken for 1 h. The supernatant was then removed by centrifugation at 3000 rpm for 10 min. The residues were washed eight times with a hydrofluoric acid solution (10%) with ultrasonication. The oscillation program consisted of the following: four × 1 h, three × 12 h, and one × 24 h. The soil samples were washed with distilled water four times to remove the residual hydrofluoric acid. The above-mentioned treated soil samples were dried in an oven at 40 °C, ground and passed through a 60-mesh sieve for NMR measurements.The soil samples were then subjected to solid-state magic-angle rotation-NMR measurements (AVANCE II 300 MH) using a 7 mm CPMAS probe with an observed frequency of 100.5 MHz, an MAS rotation frequency of 5000 Hz, a contact time of 2 s, and a cycle delay time of 2.5 s. The external standard material for the chemical shift was hexamethyl benzene (HMB, methyl 17.33 mg kg−1). The spectra were quantified by subdividing them into the following chemical shift regions55: 0–45 ppm (alkyl), 45–60 ppm (N-alkyl and methoxyl), 60–110 ppm (O-alkyl), 110–140 ppm (aryl), 140–160 ppm (O-aryl), 160–185 ppm (carboxy), and 185–230 ppm (carbonyl) (Fig. 3a). We classified O-alkyl, O-aryl, and carboxy C as labile C and alkyl, N-alkyl/methoxyl, and aryl C were classified as recalcitrant C.Soil microbial C metabolic profilesThe soil microbial C metabolic capacities were measured with BIOLOG 96-well Eco-Microplates (Biolog Inc., USA) using 31 different C sources and three replicates in each microplate. These C sources included carbohydrates, carboxylic acids, polymers, amino acids, amines, and phenolic acids (Table S2). Carbohydrates, amino acids, and carboxylic acids are generally considered labile C sources, amines and phenolic acid compounds are relatively resistant C sources, and polymers are recalcitrant C. The diverse nature of these C sources allowed us to identify differences in the capacity of microbes to degrade different C sources56. Soil microbes were extracted as follows: (1) Five grams of soil (dry weight equivalent) was incubated at 25 °C for 24 h, and 45 ml of sterile 0.85% (w/v) sodium chloride solution was added57. (2) At room temperature (25 °C), the mixture was shaken at 200 rpm for 30 min and allowed to stand for 15 min. (3) Subsequently, 0.1 ml of the supernatant was collected and diluted to 100 ml with sterile sodium chloride solution. (4) Soil suspensions were dispensed into each of the 93 wells (150 μl per well), and the plates were then incubated at 25 °C in the dark for 14 days. The optical density (OD, reflecting C utilization) of each well was read at 590 nm (color development) every 12 h. The normalized OD of different C sources was calculated as the OD of the well that contained the C source minus the OD of the well that contained sterile sodium chloride solution (control well). The normalized OD at a single time point (228 h) was used for the posterior analysis when it reached the asymptote.DNA extraction, PCR amplification, and sequencingDNA was extracted from all 90 soil samples. Briefly, well-mixed soil samples (0.6 g) were analyzed using the Power Soil DNA Isolation Kit (MoBio Laboratories, Inc., Carlsbad, CA, USA) following the manufacturer’s instructions. The quality of the DNA extracts was determined by spectrophotometry (OD-1000+, OneDrop Technologies, China). The DNA extracts were considered of sufficient quality if the ratio of OD260 to OD280 (optical density, OD) and the ratio of OD260 to OD230 were approximately 1.8. All eligible DNA samples were stored at −80 °C.Taxonomic profiling of the soil bacterial and fungal communities was performed using an Illumina® HiSeq Benchtop Sequencer. PCR amplification was performed using an ABI GeneAmp® 9700 (ABI, Foster City, CA, USA) with a 20 μl reaction system containing 4 μl of 5× FastPfu Buffer, 0.8 μl of each primer (5 μM), 2 μl of 2.5 mM dNTPs, 2 μl of template DNA, and 0.4 μl of FastPfu Polymerase. For bacterial analysis, the forward the primer 515F (GTGCCAGCMGCCGCGG) and the reverse primer 907R (CCGTCAATTCMTTTRAGTTT) were used to amplify the bacteria-specific V4-V5 hypervariable region of the 16S rRNA gene58. For fungal analysis, the internal transcribed spacer 1 (ITS1) region of the ribosomal RNA gene was amplified with primers ITS1-1737F (GGAAGTAAAAGTCGTAACAAGG) and ITS2-2043R (GCTGCGTTCTTCATCGATGC)59. The PCR protocol for bacteria consisted of an initial predenaturation step of 95 °C for 2 min, 35 cycles of 20 s at 94 °C, 40 s at 55 °C and 1 min at 72 °C, and a final 10 min extension at 72 °C. The PCR protocol for fungi consisted of an initial predenaturation step of 95 °C for 3 min, 35 cycles of 30 s at 95 °C, 30 s at 59.3 °C, and 45 s at 72 °C and a final 10 min extension at 72 °C.Each sample was independently amplified three times. Following amplification, 2 μl of each of the PCR products was checked by agarose gel (2.0%) electrophoresis, and all the PCR products from the same sample were then pooled together. The pooled mixture was purified using the Agencourt AMPure XP Kit (Beckman Coulter, CA, USA). The purified products were indexed in the 16S and ITS libraries. The quality of these libraries was assessed using Qubit@2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 systems. These pooled libraries (16S and ITS) were subsequently sequenced with an Illumina HiSeq 2500 Sequencer to generate 2 × 250 bp paired-end reads at the Center for Genetic & Genomic Analysis, Genesky Biotechnologies Inc., Shanghai, China.The raw reads were quality filtered and merged as follows: (1) TrimGalore was used for truncation of the raw reads at any site with an average quality score  5%) soils, changes in the C metabolic capacity of microbes under elevated temperatures were characterized using the ratio of the OD of microbes measured in the translocated soils to the OD of microbes in the in situ HL soil. A ratio greater than 1 indicates that translocation warming increases the C metabolism of microbes.Mantel and partial Mantel analysisA previous study showed that partial Mantel analysis is a robust method for evaluating the relationship among three variables65. This approach can control the z-axis and assess only the relationship between the x- and y-axes, avoiding the interaction between the z- and x-axes on the y-axis. In this study, Mantel analysis was employed to assess the relationships between the stability of the bacterial and fungal communities and C metabolic capacity. Stability refers primarily to the ability of the microbial community to resist translocation warming66. A higher similarity between the microbial communities in translocated soil compared with that in the in situ HL area indicates that the community is more resistant to translocation-related warming and that the microbial community is more stable.Calculation of the microbial β-diversityBray-Curtis and Euclidean dissimilarity metrics were calculated to estimate the bacterial and fungal taxonomic dissimilarity (β-diversity) and environmental dissimilarity (e.g., latitude, MAT, and MAP), respectively, using the vegan package (version 2.5–6) in the R statistical program (version 4.0.2, https://www.r-project.org/)67. Corresponding to the 45 C metabolism ratios in soils with the same OM content, the β-diversity values of bacteria and fungi were selected to analyze the relationship between the community similarity (1-β-diversity) of bacteria and fungi and changes in microbial C metabolism.Impact of the SOM content and climate change on changes in microbial communitiesThe distribution patterns of the bacterial and fungal communities under different SOM gradients and climatic regimes were determined through nonmetric multidimensional scaling (NMDS)68. To quantitatively compare the effects of the SOM gradient and climatic regimes on the bacterial and fungal community composition, three nonparametric multivariate statistical analyses were used in this study: nonparametric multivariate analysis of variance (Adonis), analysis of similarity (ANOSIM), and multiple response permutation procedure (MRPP)69. The linear fit between environmental dissimilarity and microbial β-diversity was analyzed using the lm function in R. A significant difference in the bacterial and fungal β-diversity among different SOM contents was evaluated by Student’s paired t-test using the ggpubr (version 0.4.0) package70. RDA was performed to analyze the relationships of bacterial and fungal communities with various environmental factors (soil geochemical attributes and climatic conditions, such as MAP and MAT). In parallel, the Monte Carlo permutation test (999 permutations) was employed to determine whether the explanation of the microbial distribution by individual factors (e.g., pH, SOC, and TN) was significant71.Construction of the structural equation model and random forest modelA SEM was fitted to illustrate the direct or indirect effects of soil properties (e.g., pH, moisture, ammonia, and nitrate nitrogen), climate change (e.g., MAT and MAP), and bacterial and fungal β-diversity on soil C metabolic capacity72. Based on the Euclidean method, the changes in soil properties and climatic conditions of five translocated sites compared with those in the in situ HL site were calculated. A total of 45 ratios were obtained for each OM content. Corresponding to the 45 ratios in soils with the same OM content, the β-diversity values of bacteria and fungi were selected. The model construction process was mainly divided into three steps. In brief, these steps include the establishment of an a priori model, data normality detection, and an overall goodness-of-fit test. The prior model was constructed based on a literature review and our knowledge. For the variables that did not conform to the normal distribution, we performed logarithmic transformation. Here, we used the χ2 test (the model was assumed to exhibit a good fit if p  > 0.05), the goodness-of-fit index (GFI; the model was assumed to show a good fit if GFI  > 0.9), the root mean square error of approximation (RMSEA; the model was assumed to exhibit a good fit if RMSEA  0.05)73 and the Bollen-Stine bootstrap test (the model was assumed to show a good fit if the bootstrap p  > 0.10) to test the overall goodness of fit of the SEM. All SEM analyses were conducted using IBM® SPSS® Amos 21.0 (AMOS, IBM, USA). Additionally, the importance of the metabolic capacity of different types of C on labile and recalcitrant C was assessed by random forest models using the randomForest package (version 4.6-14) in R74, and the model significance and amount of interpretation were evaluated using the rfUtilities package (version 2.1–5)75.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More