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    Gridded maps of wetlands dynamics over mid-low latitudes for 1980–2020 based on TOPMODEL

    The conceptual flow chart of the process is provided in Fig. 1. We used seven reanalysis SM data (Table 2) masked with soil temperature (ST) and soil freeze/thaw status to calculate water table depth, i.e. the input of TOPMODEL, given the obvious disagreements between the input datasets. The diagnostic algorithms based on TOPMODEL were used following Stocker et al. (ref. 20) and Xi et al. (ref. 25), where the optimized parameters were calibrated with long-term maximum wetland areas from four observation-based wetland datasets (Table 1). Details about these datasets and computational processing are shown as follows.Fig. 1Diagram of workflow for parameter calibration and the simulation of global wetland dynamics.Full size imageTable 2 Key characteristics of seven global soil moisture reanalysis data used in this study.Full size tableReanalysis soil moisture datasetsSeven long-term reanalysis SM datasets used in this study include NCEP-DOE (National Centers for Environmental Prediction-the Department of Energy)26, MERRA-Land (the Modern-Era Retrospective Analysis for Research and Applications)27, MERRA-2 (ref. 28), GLDAS-Noah v2.0 (the Global Land Data Assimilation System)29, GLDAS-Noah v2.1 (ref. 29), ERA5 (European Environment Agency)30,31, and ERA5-Land30,31. Key characteristics of the seven SM data are listed in Table 2. The datasets differ by their spatial and temporal resolutions, the time-period they cover, as well as the definition of the soil layers. More details are provided for each dataset below.

    NCEP-DOE
    NCEP-DOE is an updated version of the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis 1 project, which uses a state-of-the-art analysis/forecast system to perform data assimilation with past data from 1948 to the present32. NCEP-DOE features the newer physics and observed SM forcing and also eliminates several previous errors, such as oceanic albedo and snowmelt term during the entire period, and snow cover analysis error from 1974 to 1994 (ref. 26). With a spatial resolution of about 210 km, there are two vertical soil layers in NCEP-DOE for both SM and ST: 0–0.1 and 0.1–2 m.

    MERRA-Land and MERRA-2
    MERRA-Land soil moisture is generated by driving the Goddard Earth Observing System model version 5.7.2 (GEOS-5.7.2) with meteorological forcing from the MERRA reanalysis product27. The precipitation forcing in MERRA-Land merges MERRA precipitation with a gauge-based data product from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center, and the Catchment land surface model used in MERRA-Land is updated to the “Fortuna-2.5” version27. MERRA-2 intends to replace the original MERRA reanalysis and ingests important new data types28. The Catchment model in MERRA-2 has been updated with rainfall interception and snow model parameters of MERRA-Land, and the precipitation correction is a refined version of MERRA-Land. For MERRA-Land and MERRA-2, there is only one layer for SM from the surface to the bedrock, with “depth-to-rock” depending on local conditions. ST is computed on six vertical soil layers: 0–0.10, 0.10–0.29, 0.29–0.68, 0.68–1.44, 1.44–2.95, and 2.95–12.95 m.

    ERA5 and ERA5-Land
    ERA5 is the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis of global climate and weather, replacing ERA-Interim30,31. Based on a decade of developments in model dynamics and data assimilation, there is a significantly enhanced horizontal resolution (31 km), temporal resolution (hourly) and uncertainty estimation. ERA5 covers 1979–2020 and continues to be updated in near-real-time. ERA5-Land is produced with a finer horizontal resolution of 9 km by running the land component of the ERA5 climate reanalysis but without data assimilation. By March of 2021, the ERA5-Land outputs are only available since 1981. SM and ST are computed on four vertical soil layers (0–0.07, 0.07–0.28, 0.28–1, and 1–2.89 m) for both ERA5 and ERA5-Land.

    GLDAS-Noah v2.0 and GLDAS-Noah v2.1

    GLDAS is a global, moderate-resolution (0.25° × 0.25°) offline terrestrial modeling system developed by NASA Goddard Space Flight Center (GSFC) and the NOAA National Centers for Environmental Prediction29, thus similar to ERA5. To produce optimal fields of land surface variables in near-real-time, it incorporates satellite- and ground-based observations. GLDAS-Noah drives the Noah land surface model and has two components: one forced with the Princeton meteorological forcing data (i.e. GLDAS-Noah v2.0) and the other forced with a combination of model and observation (i.e. GLDAS-Noah v2.1). GLDAS-Noah v2.0 covers the period 1948–2014, while GLDAS-Noah v2.1 is available from 2000 to the present. There are four vertical layers in the Noah land surface model for both ST and SM: 0–0.1, 0.1–0.4, 0.4–1, and 1–2 m.Observation-based wetland/flooded area dataIn terms of large uncertainties in current wetland datasets (Table 1) we selected four widely used and available satellite/satellite-based wetland/flooded area data including GIEMS-2 (ref. 14), RFW (the Regularly Flooded Wetland map)10, WAD2M (a global dataset of Wetland Area and Dynamics for Methane Modeling)33, and G2017 (the pantropical wetland extent from an expert system model)9 for parameter calibration. Among them, GIEMS-2 and WAD2M include monthly wetland dynamics, while RFW and G2017 are static. The comparison of the four wetland datasets is shown in Supplementary Fig. 1; details on each data are provided below.

    GIEMS-2
    The GIEMS-1 is the first global estimate of monthly inundated areas, derived from passive microwave land surface emissivity34. With a 0.25° × 0.25° resolution, GIEMS-1 documents a mean annual maximum inundated area of 9.5 Mkm2 for 1993–2007 (including open water, wetlands, and rice paddies, but excluding large lakes), which shows good agreement with existing independent, static inventories as well as regional high-resolution synthetic aperture radar observations34. Based on similar retrieval principles with GIEMS-1, GIEMS-2 is developed to less depend on ancillary data with an updated microwave emissivity, and correct a known overestimation over low vegetated areas from GIEMS-1 (ref. 14). The period is extended to 1992–2015 for GIEMS-2 and can be updated with the availability of observations. Globally, the mean annual maximum and long-term maximum inundated extent after removing the rice paddies using the Monthly Irrigated and Rainfed Crop Areas dataset (MIRCA2000)35 for the period 1992–2015 are 6.7 and 10.9 million km2 (hereafter Mkm2; sum of mean annual maximum or long-term maximum inundated extent for each grid cell) respectively. The rice paddies are removed here as they are not natural wetlands and cannot be simulated with TOPMODEL.

    RFW
    RFW is a static, high-resolution map (15 arc-sec) of regularly flooded wetlands, developed by overlapping flooded areas (permanent wetlands and flooded vegetation classes) for 2008–2012 from the ESA-CCI land cover map36, mean annual maximum inundated areas (including wetlands, rivers, small lakes, and irrigated rice) for 1993–2004 from GIEMS-D15 global inundation extent (downscaled using GIEMS-1)37, and long-term maximum surface water areas for 1984–2015 from JRC global surface water bodies product13. The large permanent lakes and reservoirs are distinguished using the HydroLAKES database38. Globally, RFW covers 9.7% of the land surface area (~13.0 Mkm2) including wetlands, river channels, deltas, and flooded lake margins, but excluding large lakes10. Due to the mean annual maximum or long-term maximum inundation/surface water extent for 1984–2016 from the three wetland data is used, we treated RFW as the long-term maximum wetland extent in this study. Besides, given that GIEMS-D15 includes artificial rice paddies, we removed them with MIRCA2000 from RFW (~11.9 Mkm2 after removing rice paddies).

    WAD2M
    WAD2M dataset used in this study is an improved version of the SWAMPS v3.2 from Jensen et al. (ref. 15), covering the years 2000 to 2018. With a spatial resolution of 25 km × 25 km, this data was used as input wetland area data of phase 2 of the Global Methane Budget33. Given that the initial SWAMPS failed to detect wetlands lacking surface inundation and to differentiate between lakes, wetlands, and other surface water bodies, Zhang et al. (ref. 33) modified it using a series of independent static wetland distribution data7,9,39,40,41 in an attempt to include missing wetlands under dense canopies. Besides, they removed inland waters (lakes, rivers, and ponds) and rice agriculture with JRC and MIRCA2000, respectively. Globally, the mean annual maximum and long-term maximum wetland extent for the period 2000–2018 estimated by WAD2M are 8.1 Mkm2 and 13.2 Mkm2 (sum of mean annual maximum or long-term maximum inundated extent for each grid cell) respectively.

    G2017

    G2017 (ref. 9) is a static, pantropical wetland and peatland extent map (covering 60°S–40°N) at 232 m × 232 m resolution, derived from a hybrid expert model system. With three biophysical indices related to wetland and peat formation (long-term water supply exceeding atmospheric water demand, annually or seasonally waterlogged soils, and geomorphological position where water is supplied and retained), G2017 identifies not only permanently and seasonally wetland areas, but also soil wetness and topographic conditions that favor waterlogging in the absence of flooding for the end of the 20th century. Given the broad coverage of different types of wetlands, we also treated this map as long-term maximum wetland areas. This ‘pantropical’ data (60°S to 40°N) offers the advantage to include non-flooded wetland areas that are missed in satellite-based wetland products. However, note that not all detected wetlands or peatlands in G2017 have been observed. Rice agriculture was also removed with MIRCA2000 from G2017. The resulting wetland and peatland area for 60°S–40°N is 4.0 Mkm2.The TOPMODEL-based diagnostic modelTOPMODEL as improved by Stocker et al. (ref. 20) and Xi et al. (ref. 25) was used to calculate the inundated fraction from WTD at grid-scale in this study. Based on the assumptions that the local hydraulic gradient is approximated by the local topographic slope and the water table variations can be assimilated to a succession of steady states with uniform recharge, the classical TOPMODEL establishes an analytical relationship between the soil moisture deficit and the distributions of local topographic index within a catchment. At grid-scale, the analytical relationship can be represented as:$$CT{I}_{i}-overline{CT{I}_{x}}=mathrm{-M}left({{Gamma }}_{i}-overline{{{Gamma }}_{x}}right)$$
    (1)
    where CTI indicates the topographic index, defined as the log of the ratio of contributing area to the local slope. We used the CTI data at 500 m × 500 m resolution from Marthews et al. (ref. 22), where lakes, reservoirs, mountain glaciers, and ice caps are removed using the Global Lakes and Wetlands Database7. The (overline{CT{I}_{x}}) indicates the average of CTIi of all sub-grids (index i) within the grid cell x. M indicates a tunable parameter that describes the exponential decrease of soil transmissivity with depth21. Γi is the water table of the pixel i and (overline{{{Gamma }}_{{x}}}) is the mean water table of the grid x. When Γi is at the soil surface (i.e. Γi = 0), the threshold (CT{I}_{x}^{* }) above which all pixels are flooded for the grid x is derived:$$CT{I}_{x}^{* }=overline{CT{I}_{x}}+{rm{M}}cdot overline{{{Gamma }}_{x}}$$
    (2)
    The wetlands area is defined as the flooded areas (i.e. Γ ≤ 0), the flooded fraction in the grid x (fx) being the percentage of pixels with CTIi larger than a threshold (CT{I}_{x}^{* }):$${f}_{x}=frac{1}{{A}_{x}}{sum }_{i}{A}_{i}^{* }$$with$${A}_{i}^{* }=left{begin{array}{c}{A}_{i},if,CT{I}_{i}ge CT{I}_{x}^{* }\ 0,if,CT{I}_{i} < CT{I}_{x}^{* }end{array}right.$$ (3) To reduce the computational costs from the high-resolution CTI data for predicting long time series of wetland area, we used the asymmetric sigmoid function from Stocker et al. (ref. 20) to fit the “empirical” relationship (widehat{{Psi }}) between (widehat{f}) and Γ:$${{rm{psi }}}_{x}left({{Gamma }}_{x}right)={left(1+{v}_{x}cdot {e}^{-{k}_{x}left({{Gamma }}_{x}-{q}_{x}right)}right)}^{-1/{v}_{x}}$$ (4) where vx, kx, qx are three parameters of the function. Given a value of parameter M, the three parameters can be derived with a sequence of Γx spanning a plausible range of values (−1 m to 2 m) and corresponding fx from the initial TOPMODEL approach (Eq. (3)). Thus, the wetlands in our study are defined as the flooded area simulated by TOPMODEL. As for the range of parameter M, Stocker et al. (ref. 20) used a global uniform value for M (M = 8) after testing simulated wetland fraction for a range of M (7, 8, 9). Nevertheless, given that distinct topography, soil types, and other intrinsic characteristics in different regions, we considered M as a tunable, spatially heterogeneous, and grid-specific parameter, with a range of 1–15 following Xi et al. (ref. 25). Thus, for each grid cell x there are 15 choices for M, and then 15 sets of (vx, kx, qx). The optimized parameter combination of (vx, kx, qx) is determined by selecting minimum root-mean-square-error (RMSE) between simulated inundated fractions and observations:$$RMSE=sqrt{frac{{sum }_{i=1}^{n}{left({O}_{i}-{P}_{i}right)}^{2}}{n}}$$ (5) where Oi and Pi are observed and simulated wetland fraction, respectively. n represents the time-series length for wetland extent. For simulations calibrated with RFW and G2017, the RMSE was computed with the long-term maximum (hereafter called MAX) monthly wetland area because the two data sets are static and only record the MAX wetland extent. While for simulations calibrated with GIEMS-2 and WAD2M which include temporal variations of wetland area, we calibrated the parameters with all months, mean seasonal cycle, yearly maximum, and MAX wetland area, but only showed the optimal simulations calibrated with MAX wetland area in this work to keep consistency with RFW and G2017. Besides, to provide more choices for users, we combined all of the four wetland datasets (i.e. the union of long-term maximum wetland extent) to generate a new wetland map (hereafter called MAX_all), and then used the MAX_all to calibrate the parameters to produce seven sets of global wetland extent products with seven soil moisture datasets. The simulations calibrated with yearly maximum wetland area from GIEMS-2 and WAD2M and long-term maximum wetland area from MAX_all are also provided in our resulting products.Finally, to avoid unrealistically high wetland fraction output from the function, the simulated maximum wetland fraction fx is constrained by the observed MAX wetland area with a parameter ({f}_{x}^{max}) (Eq. (6)), which is different from Stocker et al. (ref. 20). The determination of ({f}_{x}^{max}) is analyzed in the supplemental material in detail (Supplementary Text 1). Once the value of (vx, kx, qx) are determined, the wetland fraction fx can be directly derived from the monthly water table Γx according to Eqs. (4) and (6).$${f}_{x}=minleft({{Psi }}_{x}left({{Gamma }}_{x}right),{f}_{x}^{max}right)$$ (6) Calculation of water table depthWater table depth is not computed by land surface models, given their coarse soil vertical discretization. We thus used the saturation deficit of soil moisture (θSD) as a surrogate of water table depth, θSD being defined as an index consisting of saturated volumetric water content and the “actual” soil depth modified by soil freeze/thaw status:$${theta }_{SD}={z}_{{l}_{0}}-{sum }_{l=1}^{{l}_{0}}{theta }_{l}cdot frac{Delta {z}_{l}}{{theta }_{S}}$$ (7) Subscript l represents the lth soil layer, l0 is the number of layers above the first frozen soil layer counted from the top (l = 1 at the soil surface), θl is the monthly volumetric water content in the lth soil layer (m3 m−3), (Delta {z}_{l}) is the thickness of the lth soil layer (m), θS is the saturated volumetric water content (in m3 m−3 units, uniform over depth).As formulated in Eq. (7), ({z}_{{l}_{0}}) is the thickness of all soil layers (or depth to bedrock) when there is no frozen soil layer. If there exists at least one frozen layer, ({z}_{{l}_{0}}) is set to the depth of the uppermost frozen soil layer. We excluded the frozen soil layers here given that some important wetland processes such as methane production and transport are insignificant when the soils are frozen. In high latitudes, the presence of frozen soil layers may lead to an overestimation of the wetland fraction due to relatively large θSD values even if there is little liquid soil water above the uppermost frozen soil layer. Hence, we used monthly soil temperature (ST) at 70 cm, the Global Record of Daily Landscape Freeze/Thaw Status data42, and the Köppen climate classification system43 to refine the frozen mask. When the monthly mean ST at 70 cm is below 0 °C, or soil freezing days are more than 5 in a month, or the grid is classified as the Hot desert (BWh) in the Köppen climate classification system, the wetland fraction for the grid is set to zero. However, it should be noted that the algorithm using the ST at 70 cm could omit some unfrozen soil layers above 70 cm, which could lead to bias in estimation of methane emissions from these unfrozen layers. We provided the global wetland maps in our resulting products, but the potential uncertainties in wetland estimation due to the omitted unfrozen layers should be considered, particularly at high latitudes. We used seven reanalysis SM products to compute θSD to provide the uncertainty in SM input (Table 2). All data are re-interpolated to 0.25° × 0.25° resolution.Evaluation against wetland calibration data and independent satellite productsAlthough we calibrated parameters of the TOPMODEL-based diagnostic model with the observation-based wetland data, to what extent the simulations can reproduce the spatial patterns and temporal dynamics of the calibration wetland data must be evaluated. For spatial patterns, we calculated the RMSE of wetland area between our simulations and corresponding wetland calibration data following Eq. (5), and evaluated the spatial patterns of simulated wetland extent in two wetland hotspots including Amazon basin and Western Siberia lowlands with three independent global/regional water products. For Amazon basin, we used the global surface water dataset from JRC13 (optical satellite images) and the wetland map produced using mosaics of Japanese Earth Resources Satellite (JERS-1) L-band SAR imagery from Hess et al. (ref. 44, hereafter H2015). For West Siberian lowlands, we used JRC and the Boreal–Arctic Wetland and Lake Dataset (BAWLD, only covers the north of ~55°N) produced using an expert assessment and extrapolated using random forest modelling from climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics45. For temporal dynamics, since we only used the static wetland area (long-term maximum) from all of the four observation-based wetland products to calibrate parameters, the simulated temporal dynamics can be evaluated with the two dynamic wetland products (GIEMS-2 and WAD2M). Besides, we also used the terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE), which retrieves relative change in TWS from the monthly anomalies of the Earth’s gravity field for 2003–2016 measured by the twin GRACE satellites46,47 to evaluate the simulated temporal dynamics. More

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    Optimizing plant density and balancing NPK inputs in combination with innovative fertilizer product for sustainable maize production in North China Plain

    Liu, H. et al. Optimal nitrogen input for higher efficiency and lower environmental impacts of winter wheat production in China. Agr. Ecosyst. Environ. 224, 1–11 (2016).Article 

    Google Scholar 
    Guang-hao, L., Gui-gen, C., Wei-ping, L. & Da-lei, L. Differences of yield and nitrogen use efficiency under different applications of slow-release fertilizer in spring maize. J. Integr. Agric. 20(2), 554–564 (2021).Article 

    Google Scholar 
    Kumar, V. V. Role of Rhizospheric Microbes in Soil 377–398 (Springer, 2018).Book 

    Google Scholar 
    Ullah, A. et al. Factors affecting the adoption of organic farming in Peshawar-Pakistan. Agric. Sci. 6(06), 587–593 (2015).
    Google Scholar 
    Cui, Z. et al. Pursuing sustainable productivity with millions of smallholder farmers. Nature 555(7696), 363–366 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528(7580), 51–59 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Alzaidi, A. A., Baig, M. B. & Elhag, E. A. An investigation into the farmers ’ attitudes towards organic farming in Riyadh Region–Kingdom of Saudi Arabia. Bulg. J. Agric. Sci. 19(3), 426–431 (2013).
    Google Scholar 
    Zhihui, W. et al. Combined applications of nitrogen and phosphorus fertilizers with manure increase maize yield and nutrient uptake via stimulating root growth in a long-term experiment. Pedosphere 26(1), 62–73 (2016).Article 
    CAS 

    Google Scholar 
    Guang-hao, L., Gui-gen, C., Wei-ping, L. & Da-lei, L. Differences of yield and nitrogen use efficiency under different applications of slow release fertilizer in spring maize. J. Integr. Agric. 20(2), 554–564 (2020).
    Google Scholar 
    Zant, W. Is organic fertilizer going to be helpful in bringing a green revolution to sub-Saharan Africa? Economic explorations for Malawi agriculture (Working Paper). International House Hold Survey Network (2010).Barman, M., Paul, S., Choudhury, A. G., Roy, P. & Sen, J. Biofertilizer as prospective input for sustainable agriculture in India. Int. J. Curr. Microbiol. App. Sci. 6(11), 1177–1186 (2017).Article 

    Google Scholar 
    Kalhapure, A. H., Shete, B. T. & Dhonde, M. B. Integrated nutrient management in maize (Zea Mays L.) for increasing production with sustainability. Int. J. Agric. Food Sci. Technol. 4(3), 2249–3050 (2013).
    Google Scholar 
    Nazli, R. I., Kuşvuran, A., Inal, I., Demirbaş, A. & Tansi, V. Effects of different organic materials on forage yield and quality of silage maize (Zea mays L.). Turk. J. Agric. For. 38(1), 23–31 (2014).CAS 
    Article 

    Google Scholar 
    Niu, Z. et al. Total factor productivity growth in china’s corn farming: an application of generalized productivity indicator. J. Bus. Econ. Manag. 22(5), 1189–1208 (2021).Article 

    Google Scholar 
    van Wesenbeeck, C. F. A., Keyzer, M. A., van Veen, W. C. M. & Qiu, H. Can China’s overuse of fertilizer be reduced without threatening food security and farm incomes?. Agric. Syst. 190, 103093 (2021).Article 

    Google Scholar 
    Ji, Y., Liu, H. & Shi, Y. Will China’s fertilizer use continue to decline? Evidence from LMDI analysis based on crops, regions and fertilizer types. PLoS ONE 15, e0237234 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jiao, X. et al. Grain production versus resource and environmental costs: towards increasing sustainability of nutrient use in China. J. Exp. Bot. 67(17), 4935–4949 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sher, A. et al. Response of maize grown under high plant density; performance, issues and management: a critical review. Adv. Crop Sci. Technol. 5(3), 1–8 (2017).Article 

    Google Scholar 
    De-yang, S. H. I. et al. Increased plant density and reduced N rate lead to more grain yield and higher resource utilization in summer maize. J. Integr. Agric. 15(11), 2515–2528 (2016).Article 

    Google Scholar 
    Du, X., Wang, Z., Lei, W. & Kong, L. Increased planting density combined with reduced nitrogen rate to achieve high yield in maize. Sci. Rep. 11(1), 1–12 (2021).CAS 
    Article 

    Google Scholar 
    Li, T., Zhang, W., Yin, J., Chadwick, D., Norse, D., Lu, Y., Liu, X., Chen, X., Zhang, F., Powlson, D., & Dou, Z. Enhanced-efficiency fertilizers are not a panacea for resolving the nitrogen problem (2017).Adu-gyamfi, R. et al. One-time fertilizer briquettes application for maize production in savanna agroecologies of Ghana. Soil Fertil. Crop Prod. 111(6), 3339–3350 (2019).CAS 

    Google Scholar 
    Jiang, C. et al. Optimal nitrogen application rates of one-time root zone fertilization and the effect of reducing nitrogen application on summer maize. Sustainability 11, 2979 (2019).Article 

    Google Scholar 
    Jiang, C. et al. One-time root-zone N fertilization increases maize yield, NUE and reduces soil N losses in lime concretion black soil. Sci. Rep. 8(1), 1–10 (2018).ADS 

    Google Scholar 
    Li, G., Zhao, B., Dong, S., Liu, P. & Vyn, T. J. Impact of controlled release urea on maize yield and nitrogen use efficiency under different water conditions. PLoS ONE 12(7), 1–16 (2017).
    Google Scholar 
    Sikora, J. et al. Assessment of the efficiency of nitrogen slow-release fertilizers in integrated production of carrot depending on fertilization strategy. Sustainability (Switzerland) 12(5), 1–10 (2020).
    Google Scholar 
    Tian, C. et al. Effects of a controlled-release fertilizer on yield, nutrient uptake, and fertilizer usage efficiency in early ripening rapeseed (Brassica napus L.). J. Zhejian Univ. Sci. B (Biomed. Biotechnol.) 17(14), 775–786 (2016).CAS 
    Article 

    Google Scholar 
    Tong, D. & Xu, R. Effects of urea and ( NH4)2SO4 on nitrification and acidification of Ultisols from Southern China. J. Environ. Sci. 24(4), 682–689 (2012).CAS 
    Article 

    Google Scholar 
    El-rokiek, K. G., Ahmed, S. A. & Abd-elsamad, E. E. H. Effect of adding urea or ammonium sulphate on some herbicides efficiency in controlling weeds in onion plants. J. Am. Sci. 6(11), 536–543 (2010).
    Google Scholar 
    FAO. Guidelines for soil description. Enhanced Recovery After Surgery, (2006).Landon, J. Booker Tropical Soil manual: A Handbook for Soil Survey and Agriculture Land Evaluation in the Tropics and Subtropics (2013).Zhao, R. F. et al. Fertilization and nitrogen balance in a wheat-maize rotation system in North China. Agron. J. 98(4), 938–945 (2006).CAS 
    Article 

    Google Scholar 
    Huang, S. et al. Estimation of nitrogen supply for summer maize production through a long-term field trial in china. Agronomy 11(7), 1358 (2021).CAS 
    Article 

    Google Scholar 
    Dong, Y. J. et al. Effects of new coated release fertilizer on the growth of maize. J. Soil Sci. Plant Nutr. 16(3), 637–649 (2016).CAS 

    Google Scholar 
    Ngosong, C., Bongkisheri, V., Tanyi, C. B., Nanganoa, L. T. & Tening, A. S. Optimizing nitrogen fertilization regimes for sustainable maize (Zea mays L.) production on the volcanic soils of Buea Cameroon. Adv. Agric. 2019, 1–8 (2019).
    Google Scholar 
    Su, W., Ahmad, S., Ahmad, I. & Han, Q. Nitrogen fertilization affects maize grain yield through regulating nitrogen uptake, radiation and water use efficiency, photosynthesis and root distribution. PeerJ 8, 1–21 (2020).CAS 

    Google Scholar 
    Sainju, U. M, Ghimire, R., & Pradhan, G.P. Nitrogen Fertilization I: Impact on Crop, Soil, and Environment. IntechOpen https://doi.org/10.5772/intechopen.86028 (2020).Sha, Z. et al. Effect of N stabilizers on fertilizer-N fate in the soil-crop system: a meta- analysis. Agr. Ecosyst. Environ. 2020, 290 (2019).
    Google Scholar 
    Chen, K. & Vyn, T. J. Post-silking factor consequences for N efficiency changes over 38 years of commercial maize hybrids. Front. Plant Sci. https://doi.org/10.3389/fpls.2017.01737 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jia, X. P. et al. Farmer’s adoption of improved nitrogen management strategies in maize production in China: an experimental knowledge training. J. Integr. Agric. 12(2), 364–373 (2013).Article 

    Google Scholar 
    Amanullah,. Rate and timing of nitrogen application influence partial factor productivity and agronomic NUE of maize (Zea mays L.) planted at low and high densities on calcareous soil in northwest Pakistan. J. Plant Nutr. 39(5), 683–690 (2016).CAS 
    Article 

    Google Scholar 
    Draman, A., Almas, L. K. Partial factor productivity, agronomic efficiency, and economic analyses of maize in wheat-maize cropping system in Pakistan. Southern Agricultural Economics Association Annual Meetings, 2009 (January 2009).Yan, P. et al. Interaction between plant density and nitrogen management strategy in improving maize grain yield and nitrogen use efficiency on the North China Plain. Agric. Sci. 154, 978–988 (2016).Article 

    Google Scholar 
    Oenema, O. Nitrogen use efficiency (NUE) an indicator for the utilization of nitrogen in food systems. EU Nitrogen Expert Panel, January 2017, 1–4 (2015).Venterea, R. T., Coulter, J. A. & Dolan, M. S. Evaluation of intensive “4R” strategies for decreasing nitrous oxide emissions and nitrogen surplus in rainfed corn. J. Environ. Qual. 45(4), 1186–1195 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhang, C., Ju, X., Powlson, D., Oenema, O. & Smith, P. Nitrogen surplus benchmarks for controlling N pollution in the main cropping systems of China. Environ. Sci. Technol. 53(12), 6678–6687 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Fernández, C., Koop, G. & Steel, M. F. J. Multiple-output production with undesirable outputs multiple-output production with undesirable outputs : an application to nitrogen surplus in agriculture. J. Am. Stat. Assoc. 97(458), 432–442 (2013).MATH 
    Article 

    Google Scholar 
    Børsting, C. F., Kristensen, T., Misciattelli, L., Hvelplund, T. & Weisbjerg, M. R. Reducing nitrogen surplus from dairy farms. Effects of feeding and management. Livest. Prod. Sci. 83(2–3), 165–178 (2003).Article 

    Google Scholar 
    Liang, K. et al. Reducing nitrogen surplus and environmental losses by optimized nitrogen and water management in double rice cropping system of South China. Agric. Ecosyst. Environ. 286, 106680 (2019).CAS 
    Article 

    Google Scholar 
    Klages, S. et al. Nitrogen surplus-a unified indicator for water pollution in Europe?. Water (Switzerland) 12(4), 1197 (2020).CAS 

    Google Scholar 
    Muratoglu, A. Grey water footprint of agricultural production: an assessment based on nitrogen surplus and high-resolution leaching runoff fractions in Turkey. Sci. Total Environ. 742, 140553 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Niemiec, M. & Komorowska, M. The use of slow-release fertilizers as a part of optimization of celeriac production technology. Agric. Eng. 22(2), 59–68 (2018).
    Google Scholar 
    Ranum, P., Peña-Rosas, J. P. & Garcia-Casal, M. N. Global maize production, utilization, and consumption. Ann. N. Y. Acad. Sci. 1312(1), 105–112 (2014).ADS 
    PubMed 
    Article 

    Google Scholar 
    HLPE. Biofules and food security. High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security, Rome (2013).Karp, A., Beale, M. H., Beaudoin, F. & Eastmond, P. J. Growing innovations for the bioeconomy. Nat. Plants https://doi.org/10.1038/nplants.2015.193 (2015).Article 
    PubMed 

    Google Scholar 
    Chavarria, H., Trigo, E., Villarreal, F., Elverdin, P., & Piñeiro, V. Policy brief bioeconomy: a sustainable development strategy task force 10 sustainable energy, water, and food systems. T20, Saudi Arabia (2020). More

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    SEM/EDX analysis of stomach contents of a sea slug snacking on a polluted seafloor reveal microplastics as a component of its diet

    Derraik, J. G. The pollution of the marine environment by plastic debris: A review. Mar. Pollut. Bull. 44(9), 842–852 (2002).CAS 
    PubMed 

    Google Scholar 
    Gregory, M. R. Environmental implications of plastic debris in marine settings—Entanglement, ingestion, smothering, hangers-on, hitch-hiking and alien invasions. Philos. Trans. R. Soc. B Biol. Sci. 364(1526), 2013–2025 (2009).
    Google Scholar 
    Claessens, M., Van Cauwenberghe, L., Vandegehuchte, M. B. & Janssen, C. R. New techniques for the detection of microplastics in sediments and field collected organisms. Mar. Pollut. Bull. 70(1–2), 227–233 (2013).CAS 
    PubMed 

    Google Scholar 
    Auta, H. S., Emenike, C. U. & Fauziah, S. H. Distribution and importance of microplastics in the marine environment: A review of the sources, fate, effects, and potential solutions. Environ. Int. 102, 165–176 (2017).CAS 
    PubMed 

    Google Scholar 
    Zobkov, M. B. & Esiukova, E. E. Microplastics in a Marine Environment: Review of Methods for Sampling, Processing, and Analyzing Microplastics in Water, Bottom Sediments, and Coastal Deposits (2018).Coyle, R., Hardiman, G. & O’Driscoll, K. Microplastics in the marine environment: A review of their sources, distribution processes, uptake and exchange in ecosystems. Case Stud. Chem. Environ. Eng. 2, 100010 (2020).
    Google Scholar 
    Barnes, D. K., Galgani, F., Thompson, R. C. & Barlaz, M. Accumulation and fragmentation of plastic debris in global environments. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364, 1985–1998 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    GESAMP. Sources, Fate and Effects of Microplastics in the Marine Environment: Part 2 of a Global Assessment. A Report to Inform the Second United Nations Environment Assembly, 220 (Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection, 2016).
    Google Scholar 
    Kroon, F. J., Motti, C. E., Jensen, L. H. & Berry, K. L. Classification of marine microdebris: A review and case study on fish from the Great Barrier Reef, Australia. Sci. Rep. 8(1), 1–15. https://doi.org/10.1038/s41598-018-34590-6 (2018).CAS 
    Article 

    Google Scholar 
    Cole, M., Lindeque, P., Halsband, C. & Galloway, T. Microplastics as contaminants in the marine environment: A review. Mar. Pollut. Bull. 62(12), 2588–2597 (2011).CAS 
    PubMed 

    Google Scholar 
    Cole, M. A novel method for preparing microplastic fibers. Sci. Rep. 6(1), 1–7. https://doi.org/10.1038/srep34519 (2016).CAS 
    Article 

    Google Scholar 
    Costa, M. et al. On the importance of size of plastic fragments and pellets on the strandline: A snapshot of a Brazilian beach. Environ. Monit. Assess. 168, 299–304 (2010).PubMed 

    Google Scholar 
    Kershaw, P. J. et al. (eds) GESAMP Guidelines or the Monitoring and Assessment of Plastic Litter and Microplastics in the Ocean, Rep. Stud. GESAMP No. 99 130 (IMO/FAO/UNESCO-IOC/UNIDO/WMO/IAEA/UN/UNEP/UNDP/ISA Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection, 2019).
    Google Scholar 
    Lusher, A. L., Welden, N. A., Sobral, P. & Cole, M. Sampling, isolating and identifying microplastics ingested by fish and invertebrates. Anal. Methods 9, 1346 (2017).
    Google Scholar 
    Lusher, A., Bråte, I. L. N., Hurley, R., Iversen, K. & Olsen, M. Testing of Methodology for Measuring Microplastics in Blue Mussels (Mytilus spp) and Sediments, and Recommendations for Future Monitoring of Microplastics (R & D-project) (2017).Laist, D. W. Impacts of marine debris: Entanglement of marine life in marine debris including a comprehensive list of species with entanglement and ingestion records. In Marine debris, 99–139 (Springer, 1997).Denuncio, P. et al. Plastic ingestion in Franciscana dolphins, Pontoporia blainvillei (Gervais and d’Orbigny, 1844), from Argentina. Mar. Pollut. Bull. 62(8), 1836–1841 (2011).CAS 
    PubMed 

    Google Scholar 
    Do Sul, J. A. I., Santos, I. R., Friedrich, A. C., Matthiensen, A. & Fillmann, G. Plastic pollution at a sea turtle conservation area in NE Brazil: Contrasting developed and undeveloped beaches. Estuar. Coasts 34(4), 814–823 (2011).
    Google Scholar 
    Lazar, B. & Gračan, R. Ingestion of marine debris by loggerhead sea turtles, Caretta caretta, in the Adriatic Sea. Mar. Pollut. Bull. 62(1), 43–47 (2011).CAS 
    PubMed 

    Google Scholar 
    Poppi, L. et al. Post-mortem investigations on a leatherback turtle Dermochelys coriacea stranded along the Northern Adriatic coastline. Dis. Aquat. Org. 100(1), 71–76 (2012).
    Google Scholar 
    Van Franeker, J. A. et al. Monitoring plastic ingestion by the northern fulmar Fulmarus glacialis in the North Sea. Environ. Pollut. 159(10), 2609–2615 (2011).PubMed 

    Google Scholar 
    Betts, K. Why Small Plastic Particles May Pose a Big Problem in the Oceans 8995–8995 (ACS Publications, 2008).
    Google Scholar 
    Cefas, L. Programme 8: Bass gillnet selectivity. Fish. Sci. 09 (2008).Priscilla, V., Sedayu, A. & Patria, M. P. Microplastic abundance in the water, seagrass, and sea hare Dolabella auricularia in Pramuka Island, Seribu Islands, Jakarta Bay, Indonesia. J. Phys. Conf. Ser. 1402, 033073. https://doi.org/10.1088/1742-6596/1402/3/033073 (2019).Article 

    Google Scholar 
    Graham, E. R. & Thompson, J. T. Deposit-and suspension-feeding sea cucumbers (Echinodermata) ingest plastic fragments. J. Exp. Mar. Biol. Ecol. 368(1), 22–29 (2009).
    Google Scholar 
    Thompson, R. C. et al. Lost at sea: Where is all the plastic? Science 304(5672), 838–838 (2004).CAS 
    PubMed 

    Google Scholar 
    Hämer, J., Gutow, L., Köhler, A. & Saborowski, R. Fate of microplastics in the marine isopod Idotea emarginata. Environ. Sci. Technol. 48(22), 13451–13458 (2014).ADS 
    PubMed 

    Google Scholar 
    Setälä, O., Fleming-Lehtinen, V. & Lehtiniemi, M. Ingestion and transfer of microplastics in the planktonic food web. Environ. Pollut. 185, 77–83 (2014).PubMed 

    Google Scholar 
    Cole, M. et al. Microplastics alter the properties and sinking rates of zooplankton faecal pellets. Environ. Sci. Technol. 50(6), 3239–3246 (2016).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gutow, L., Eckerlebe, A., Giménez, L. & Saborowski, R. Experimental evaluation of seaweeds as a vector for microplastics into marine food webs. Environ. Sci. Technol. 50(2), 915–923 (2016).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Naji, A., Nuri, M. & Vethaak, A. D. Microplastics contamination in molluscs from the northern part of the Persian Gulf. Environ. Pollut. 235, 113–120 (2018).CAS 
    PubMed 

    Google Scholar 
    Ding, J. et al. Detection of microplastics in local marine organisms using a multi-technology system. Anal. Methods 11(1), 78–87 (2019).CAS 

    Google Scholar 
    Gniadek, M. & Dąbrowska, A. The marine nano-and microplastics characterisation by SEM-EDX: The potential of the method in comparison with various physical and chemical approaches. Mar. Pollut. Bull. 148, 210–216 (2019).CAS 
    PubMed 

    Google Scholar 
    Dąbrowska, A. A roadmap for a plastisphere. Mar. Pollut. Bull. 167, 112322 (2021).PubMed 

    Google Scholar 
    Ebere, E. C. & Ngozi, V. E. Microplastics, an emerging concern: A review of analytical techniques for detecting and quantifying microplatics. Anal. Methods Environ. Chem. J. 2(2), 13–30 (2019).
    Google Scholar 
    Mariano, S., Tacconi, S., Fidaleo, M., Rossi, M. & Dini, L. Micro and nanoplastics identification: Classic methods and innovative detection techniques. Front. Toxicol. https://doi.org/10.3389/ftox.2021.636640 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ferrante, M. et al. Microplastics in fillets of Mediterranean seafood. A risk assessment study. Environ. Res. 204, 112247 (2022).CAS 
    PubMed 

    Google Scholar 
    Li, J. et al. Characterization, source, and retention of microplastic in sandy beaches and mangrove wetlands of the Qinzhou Bay, China. Mar. Pollut. Bull. 136, 401–406 (2018).CAS 
    PubMed 

    Google Scholar 
    Liu, J. et al. Pollution characteristics of microplastics in mollusks from the coastal Area of Yantai. China. Bull. Environ. Contamin. Toxicol. 107, 1–7 (2021).
    Google Scholar 
    Tarjuelo, I., Posada, D., Crandall, K., Pascual, M. & Turon, X. Cryptic species of Clavelina (Ascidiacea) in two different habitats: Harbours and rocky littoral zones in the northwestern Mediterranean. Mar. Biol. 139(3), 455–462 (2001).
    Google Scholar 
    Brunetti, R. & Mastrototaro, F. Botrylloides pizoni, a new species of Botryllinae (Ascidiacea) from the Mediterranean Sea R. Zootaxa 3258(1), 28–36 (2012).
    Google Scholar 
    Beli, E. et al. The zoogeography of extant rhabdopleurid hemichordates (Pterobranchia: Graptolithina), with a new species from the Mediterranean Sea. Invertebr. Syst. 32(1), 100–110 (2018).
    Google Scholar 
    Chimienti, G., Angeletti, L., Furfaro, G., Canese, S. & Taviani, M. Habitat, morphology and trophism of Tritonia callogorgiae sp. nov., a large nudibranch inhabiting Callogorgia verticillata forests in the Mediterranean Sea. Deep Sea Res. I Oceanogr. Res. Pap. 165, 103364 (2020).
    Google Scholar 
    Furfaro, G. & Mariottini, P. A new Dondice Marcus Er. 1958 (Gastropoda: Nudibranchia) from the Mediterranean Sea reveals interesting insights into the phylogenetic history of a group of Facelinidae taxa. Zootaxa 4731(1), 1–22. https://doi.org/10.11646/zootaxa.4731.1.1 (2020).Article 

    Google Scholar 
    Cózar, A. et al. Plastic accumulation in the Mediterranean Sea. PLoS ONE 10(4), e0121762. https://doi.org/10.1371/journal.pone.0121762 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sharma, S., Sharma, V. & Chatterjee, S. Microplastics in the Mediterranean Sea: Sources, pollution intensity, sea health, and regulatory policies. Front. Mar. Sci. 8, 634934. https://doi.org/10.3389/fmars.2021.634934 (2021).Article 

    Google Scholar 
    Pinardi, N. & Masetti, E. Variability of the large scale general circulation of the Mediterranean Sea from observations and modelling: A review. Palaeogeogr. Palaeoclimatol. Palaeoecol. 158(3–4), 153–173 (2000).
    Google Scholar 
    Suaria, G. et al. The Mediterranean Plastic soup: Synthetic polymers in Mediterranean surface waters. Sci. Rep. 6(1), 1–10 (2016).
    Google Scholar 
    Vianello, A. et al. Microplastic particles in sediments of Lagoon of Venice, Italy: First observations on occurrence, spatial patterns and identification. Estuar. Coast. Shelf. Sci. 130, 54–61. https://doi.org/10.1016/j.ecss.2013.03.022 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    Parenzan, P. Il Mar Piccolo di Taranto. Ciem. Comm. Taranto (1984).Cavallo, R. A. & Stabili, L. Presence of vibrios in seawater and Mytilus galloprovincialis (Lam.) from the Mar Piccolo of Taranto (Ionian Sea). Water Res. 36(15), 3719–3726 (2002).CAS 
    PubMed 

    Google Scholar 
    Cardellicchio, N. et al. Organic pollutants (PAHs, PCBs) in sediments from the Mar Piccolo in Taranto (Ionian Sea, Southern Italy). Mar. Pollut. Bull. 55(10–12), 451–458 (2007).CAS 
    PubMed 

    Google Scholar 
    Cardellicchio, N., Annicchiarico, C., Di Leo, A., Giandomenico, S. & Spada, L. The Mar Piccolo of Taranto: An interesting marine ecosystem for the environmental problems studies. Environ. Sci. Pollut. Res. 23(13), 12495–12501 (2016).
    Google Scholar 
    Tursi, A. et al. Mega-litter and remediation: The case of Mar Piccolo of Taranto (Ionian Sea). Rendiconti Lincei. Sci. Fisiche e Nat. 29(4), 817–824 (2018).
    Google Scholar 
    Mastrototaro, F. et al. Benthic diversity of the soft bottoms in a semi-enclosed basin of the Mediterranean Sea. Marine Biological Association of the United Kingdom. J. Mar. Biol. Assoc. U.K. 88(2), 247 (2008).
    Google Scholar 
    Li, J. et al. Using mussel as a global bioindicator of coastal microplastic pollution. Environ. Pollut. 244, 522–533 (2019).CAS 
    PubMed 

    Google Scholar 
    Corami, F. et al. Evidence of small microplastics (< 100 μm) ingestion by Pacific oysters (Crassostrea gigas): A novel method of extraction, purification, and analysis using Micro-FTIR. Mar. Pollut. Bull. 160, 111606 (2020).CAS  PubMed  Google Scholar  De-la-Torre, G. E., Apaza-Vargas, D. M. & Santillán, L. L. Microplastic ingestion and feeding ecology in three intertidal mollusk species from Lima, Peru. Rev. Biol. Mar. Oceanogr. 55(2), 167–171 (2020). Google Scholar  Jiang, Y. et al. A review of microplastic pollution in seawater, sediments and organisms of the Chinese coastal and marginal seas. Chemosphere 286, 131677 (2021).ADS  PubMed  Google Scholar  Haszprunar, G. The heterobranchia—A new concept of the phylogeny of the higher Gastropoda. J. Zool. Syst. Evol. Res. 23(1), 15–37 (1985). Google Scholar  Wägele, H., Klussmann-Kolb, A., Vonnemann, V. & Medina, M. Heterobranchia I: The Opisthobranchia. In Phylogeny and Evolution of the Mollusca (eds Ponder, W. F. & Lindberg, D.) 385–408 (University of California Press, 2008). Google Scholar  Prkic, J. et al. First record of Calma gobioophaga Calado and Urgorri, 2002 (Gastropoda: Nudibranchia) in the Mediterranean Sea. Mediterr. Mar. Sci. 15(2), 423–428 (2014). Google Scholar  Furfaro, G., Trainito, E., De Lorenzi, F., Fantin, M. & Doneddu, M. Tritonia nilsodhneri Marcus Ev., 1983 (Gastropoda, Heterobranchia, Tritoniidae): First records for the Adriatic Sea and new data on ecology and distribution of Mediterranean populations. Acta Adriat. 58, 2 (2017). Google Scholar  Thompson, T. E. Studies on ontogeny of Tritonia hombergi Cuvier (Gastropoda: Opisthobranchia). Philos. Trans. R. Soc. Lond. B 245, 171–218. https://doi.org/10.1098/rstb.1962.0009 (1962).ADS  Article  Google Scholar  Cattaneo-Vietti, R., Angelini, S. & Bavestrello, G. Skin and gut spicules in Discodoris atromaculata (Bergh, 1880) (Mollusca: Nudibranchia). Bollettino Malacol. 28, 173–180 (1993). Google Scholar  Cattaneo-Vietti, R., Angelini, S., Gaggero, L. & Lucchetti, G. Mineral composition of nudibranch spicules. J. Molluscan Stud. 61(3), 331–337. https://doi.org/10.1093/mollus/61.3.331 (1995).Article  Google Scholar  Garese, A., García-Matucheski, S., Acuña, F. H. & Muniain, C. Feeding behavior of Spurilla sp. (Mollusca: Opisthobranchia) with a description of the kleptocnidae sequestered from its sea anemone prey. Zool. Stud. 51(7), 905–912 (2012).CAS  Google Scholar  Braga, T. et al. Bursatella leachii from Mar Menor as a source of bioactive molecules: Preliminary evaluation of the nutritional profile, in vitro biological activities and fatty acids contents. J. Aquat. Food Prod. Technol. 26(10), 1337–1350 (2017).CAS  Google Scholar  Willis, T. J. et al. Kleptopredation: A mechanism to facilitate planktivory in a benthic mollusc. Biol. Let. 13, 20170447. https://doi.org/10.1098/rsbl.2017.0447 (2017).Article  Google Scholar  Goodheart, J. A. et al. Comparative morphology and evolution of the cnidosac in Cladobranchia (Gastropoda: Heterobranchia: Nudibranchia). Front. Zool. 15(1), 1–18. https://doi.org/10.1186/s12983-018-0289-2 (2018).CAS  Article  Google Scholar  Marin, A. & Ros, J. Chemical defenses in Sacoglossan Opisthobranchs: Taxonomic trends and evolutive implications. Sci. Mar. 67(Suppl. 1), 227–241 (2004). Google Scholar  Wägele, H., Ballestero, M. & Avila, C. Defensive glandular structures in opisthobranch molluscs—From histology to ecology. Oceanogr. Mar. Biol. Annu. Rev. 44, 197–276 (2006). Google Scholar  Pavlik, J. R. Antipredatory defensive roles of natural products from marine invertebrates. In Handbook of Marine Natural Products Vol. 12 (eds Fattorusso, E. et al.) 677–710 (Springer, 2012). Google Scholar  Avila, C., Nuñez-Pons, L. & Moles, J. From the tropics to the poles chemical defense strategies in sea slugs (Mollusca: Heterobranchia). In Chemical Ecology: The Ecological Impact of Marine Natural Products (eds Puglisi, M. P. & Becerro, M. A.) 93 (CRC Press, 2018). Google Scholar  Capper, A., Tibbetts, I. R., O’Neil, J. M. & Shaw, G. R. The fate of Lyngbya majuscula toxins in three potential consumers. J. Chem. Ecol. 31(7), 1595–1606 (2005).CAS  PubMed  Google Scholar  Dean, L. J. & Prinsep, M. R. The chemistry and chemical ecology of nudibranchs. Nat. Prod. Rep. 34(12), 1359–1390 (2017).CAS  PubMed  Google Scholar  Simmons, T. L., Andrianasolo, E., McPhail, K., Flatt, P. & Gerwick, W. H. Marine natural products as anticancer drugs. Mol. Cancer Ther. 4(2), 333–342 (2005).CAS  PubMed  Google Scholar  Klussmann-Kolb, A. Phylogeny of the Aplysiidae (Gastropoda, Opisthobranchia) with new aspects of the evolution of seahares. Zool. Scr. 33, 439–462 (2004). Google Scholar  Willan, R. C. Phylogenetic systematics of the Notaspidea (Opisthobranchia) with reappraisal of families and genera. Am. Malacol. Bull. 5, 215–241 (1987). Google Scholar  Medina, M. & Walsh, P. J. Molecular systematics of the order Anaspidea based on mitochondrial DNA sequences (12S, 16S, and COI). Mol. Phylogenet. Evol. 15, 41–58 (2000).CAS  PubMed  Google Scholar  Furfaro, G., De Matteo, S., Mariottini, P. & Giacobbe, S. Ecological notes of the alien species Godiva quadricolor (Gastropoda: Nudibranchia) occurring in Faro Lake (Italy). J. Nat. Hist. 52(11–12), 645–657 (2018). Google Scholar  Appleton, D. R., Sewell, M. A., Berridge, M. V. & Copp, B. R. A new biologically active malyngamide from a New Zealand collection of the sea hare Bursatella leachii. J. Nat. Prod. 65(4), 630–631 (2002).CAS  PubMed  Google Scholar  Rajaganapathi, J., Kathiresan, K. & Singh, T. P. Purification of anti-HIV protein from purple fluid of the sea hare Bursatella leachii de Blainville. Mar. Biotechnol. 4(5), 447–453 (2002).CAS  Google Scholar  Suntornchashwej, S., Chaichit, N., Isobe, M. & Suwanborirux, K. Hectochlorin and morpholine derivatives from the Thai Sea Hare, Bursatella leachii. J. Nat. Prod. 68(6), 951–955 (2005).CAS  PubMed  Google Scholar  Dhahri, M. et al. Extraction, characterization, and anticoagulant activity of a sulfated polysaccharide from Bursatella leachii viscera. ACS Omega 5(24), 14786–14795 (2020).CAS  PubMed  PubMed Central  Google Scholar  Clarke, C. L. The population dynamics and feeding preferences of Bursatella leachii (Opisthobranchia: Anaspidea) in northeast Queensland, Australia. Rec. West. Austral. Museum Suppl. 69, 11–21 (2006). Google Scholar  Blainville, H. M. D. de. Bursatella, p. 138, in: Dictionnaire des Sciences Naturelles (F. Cuvier, ed.), Vol. 5, Supplément. Levrault, Strasbourg & Le Normant, Paris (1817).Trainito, E. & Doneddu, M. Nudibranchi del Mediterraneo 2nd edn, 192 (Il Castello, 2014). Google Scholar  Zbyszewski, M., Corcoran, P. L. & Hockin, A. Comparison of the distribution and degradation of plastic debris along shorelines of the Great Lakes, North America. J. Great Lakes Res. 40(2), 288–299 (2014).CAS  Google Scholar  Wang, Z. M., Wagner, J., Ghosal, S., Bedi, G. & Wall, S. SEM/EDS and optical microscopy analyses of microplastics in ocean trawl and fish guts. Sci. Total Environ. 603, 616–626 (2017).ADS  PubMed  Google Scholar  Gewert, B., Plassmann, M. & MacLeod, M. Pathways for degradation of plastic polymers floating in the marine environment. Environ. Sci. Process. Impacts 17, 1513–1521 (2015).CAS  PubMed  Google Scholar  Gewert, B., Plassmann, M., Sandblom, O. & MacLeod, M. Identification of chain scission products released to water by plastic exposed to ultraviolet light. Environ. Sci. Technol. Lett. 5, 272–276 (2018).CAS  Google Scholar  Lang, M. et al. Fenton aging significantly affects the heavy metal adsorption capacity of polystyrene microplastics. Sci. Total Environ. 722, 137762 (2020).ADS  CAS  PubMed  Google Scholar  Ding, L., Mao, R., Ma, S., Guo, X. & Zhu, L. High temperature depended on the ageing mechanism of microplastics under different environmental conditions and its effect on the distribution of organic pollutants. Water Res. 174, 115634 (2020).CAS  PubMed  Google Scholar  Wang, F. et al. The influence of polyethylene microplastics on pesticide residue and degradation in the aquatic environment. J. Hazard. Mater. 394, 122517 (2020).CAS  PubMed  Google Scholar  Ouyang, Z. et al. The aging behavior of polyvinyl chloride microplastics promoted by UV-activated persulfate process. J. Hazard. Mater. 424, 127461 (2022).CAS  PubMed  Google Scholar  Dehaut, A. et al. Microplastics in seafood: Benchmark protocol for their extraction and characterization. Environ. Pollut. 215, 223–233 (2016).CAS  PubMed  Google Scholar  Besley, A., Vijver, M. G., Behrens, P. & Bosker, T. A standardized method for sampling and extraction methods for quantifying microplastics in beach sand. Mar. Pollut. Bull. 114(1), 77–83 (2017).CAS  PubMed  Google Scholar  Karami, A. et al. A high-performance protocol for extraction of microplastics in fish. Sci. Total Environ. 578, 485–494 (2017).ADS  CAS  PubMed  Google Scholar  Caron, A. G. et al. Ingestion of microplastic debris by green sea turtles (Chelonia mydas) in the Great Barrier Reef: Validation of a sequential extraction protocol. Mar. Pollut. Bull. 127, 743–751 (2018).CAS  PubMed  Google Scholar  Piarulli, S. et al. Microplastic in wild populations of the omnivorous crab Carcinus aestuarii: A review and a regional-scale test of extraction methods, including microfibres. Environ. Pollut. 251, 117–127 (2019).CAS  PubMed  Google Scholar  Pfohl, P. et al. Microplastic extraction protocols can impact the polymer structure. Microplast. Nanoplast. 1(1), 1–13 (2021). Google Scholar  Qiu, Q. et al. Extraction, enumeration and identification methods for monitoring microplastics in the environment. Estuar. Coast. Shelf Sci. 176, 102–109 (2016).ADS  CAS  Google Scholar  Lusher, A. L., Munno, K., Hermabessiere, L. & Carr, S. Isolation and extraction of microplastics from environmental samples: An evaluation of practical approaches and recommendations for further harmonization. Appl. Spectrosc. 74(9), 1049–1065 (2020).ADS  CAS  PubMed  Google Scholar  Bellasi, A., Binda, G., Pozzi, A., Boldrocchi, G. & Bettinetti, R. The extraction of microplastics from sediments: An overview of existing methods and the proposal of a new and green alternative. Chemosphere 278, 130357 (2021).ADS  CAS  PubMed  Google Scholar  Essa, A. M. & Khallaf, M. K. Antimicrobial potential of consolidation polymers loaded with biological copper nanoparticles. BMC Microbiol. 16(1), 1–8 (2016). Google Scholar  Etcheverry, M., Ferreira, M. L., Capiati, N. J., Pegoretti, A. & Barbosa, S. E. Strengthening of polypropylene–glass fiber interface by direct metallocenic polymerization of propylene onto the fibers. Compos. A Appl. Sci. Manuf. 39(12), 1915–1923 (2008). Google Scholar  Ivanič, A., Kravanja, G., Kidess, W., Rudolf, R. & Lubej, S. The influences of moisture on the mechanical, morphological and thermogravimetric properties of mineral wool made from basalt glass fibers. Materials 13(10), 2392 (2020).ADS  PubMed Central  Google Scholar  Kavad, B. V., Pandey, A. B., Tadavi, M. V. & Jakharia, H. C. A review paper on effects of drilling on glass fiber reinforced plastic. Procedia Technol. 14, 457–464 (2014). Google Scholar  Alsayed, S. H., Al-Salloum, Y. A. & Almusallam, T. H. Performance of glass fiber reinforced plastic bars as a reinforcing material for concrete structures. Compos. B Eng. 31(6–7), 555–567 (2000). Google Scholar  Fries, E. et al. Identification of polymer types and additives in marine microplastic particles using pyrolysis-GC/MS and scanning electron microscopy. Environ. Sci. Process Impacts 15(10), 1949–1956 (2013).CAS  PubMed  Google Scholar  Turner, A. & Filella, M. The influence of additives on the fate of plastics in the marine environment, exemplified with barium sulphate. Mar. Pollut. Bull. 158, 111352 (2020).CAS  PubMed  Google Scholar  Barathi, M., Kumar, A. S. K. & Rajesh, N. Efficacy of novel Al–Zr impregnated cellulose adsorbent prepared using microwave irradiation for the facile defluoridation of water. J. Environ. Chem. Eng. 1(4), 1325–1335 (2013).CAS  Google Scholar  Bahsis, L. et al. Cellulose-copper as bio-supported recyclable catalyst for the clickable azide-alkyne [3+2] cycloaddition reaction in water. Int. J. Biol. Macromol. 119, 849–856 (2018).CAS  PubMed  Google Scholar  Ibrahim, N. A., Eid, B. M., Abd El-Aziz, E., Abou Elmaaty, T. M. & Ramadan, S. M. Multifunctional cellulose-containing fabrics using modified finishing formulations. RSC Adv. 7(53), 33219–33230 (2017).ADS  CAS  Google Scholar  Van, H. T., Le Sy, H., Nguyen, T. M. L. & Nguyen, D. K. Application of Mussell-derived biosorbent to remove NH 4+ from aqueous solution: Equilibrium and Kinetics. SN Appl. Sci. 3(4), 1–12 (2021). Google Scholar  Lakshmanna, B. et al. Data on Molluscan Shells in parts of Nellore Coast, southeast coast of India. Data Brief 16, 705–712 (2018).CAS  PubMed  Google Scholar  Taylor, P. D., Vinn, O., Kudryavtsev, A. & Schopf, J. W. Raman spectroscopic study of the mineral composition of cirratulid tubes (Annelida, Polychaeta). J. Struct. Biol. 171(3), 402–405 (2010).CAS  PubMed  Google Scholar  Schröder, V. et al. Micromorphological details and identification of chitinous wall structures in Rapana venosa (Gastropoda, Mollusca) egg capsules. Sci. Rep. 10(1), 1–13 (2020). Google Scholar  Ngamniyom, A., Wongroj, W., Karnchaisri, K. & Siriwattanarat, R. Ophidascaris baylisi (Nematoda: Ascarididae): Scanning electron microscopic study of the adult surface with ultrastructure and chemical composition analysis of eggshells. Sci. Technol. Asia 26, 189–198 (2021). Google Scholar  Fabra, M. et al. The plastic Trojan horse: Biofilms increase microplastic uptake in marine filter feeders impacting microbial transfer and organism health. Sci. Total Environ. 797, 149217 (2021).ADS  CAS  PubMed  Google Scholar  Jacquin, J. et al. Microbial ecotoxicology of marine plastic debris: A review on colonization and biodegradation by the “Plastisphere”. Front. Microbiol. 10, 865 (2019).PubMed  PubMed Central  Google Scholar  More

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    Comparative metagenomics reveals expanded insights into intra- and interspecific variation among wild bee microbiomes

    Engel, M. S. A new interpretation of the oldest fossil bee (Hymenoptera: Apidae). Am. Mus. Novit. 3296, 1–11 (2000).Article 

    Google Scholar 
    Michener, C. D. The Bees of the World 2nd edn, (John Hopkins University Press, 2007).Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B. 274, 303–313 (2007).PubMed 
    Article 

    Google Scholar 
    Fürst, M., McMahon, D. P., Osborne, J. L., Paxton, R. J. & Brown, M. J. F. Disease associations between honeybees and bumblebees as a threat to wild pollinators. Nature 506, 364–366 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    McMahon, D. P., Wilfert, L., Paxton, R. J. & Brown, M. J. F. Emerging viruses in bees: from molecules to ecology. Adv. Virus Res. 101, 251–291 (2015).Article 

    Google Scholar 
    Koch, H., Abrol, D. P., Li, J. & Schmid-Hempel, P. Diversity of evolutionary patterns of bacterial gut associates of corbiculate bees. Mol. Ecol. 22, 2028–2044 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    McFrederick, Q. S. et al. Environment or kin: whence do bees obtain acidophilic bacteria? Mol. Ecol. 21, 1754–1768 (2012).PubMed 
    Article 

    Google Scholar 
    McFrederick, Q. S., Wcislo, W. T., Hout, M. C. & Mueller, U. G. Host species and developmental stage, but not host social structure, affects bacterial community structure in social polymorphic bees. FEMS Microbiol. Ecol. 88, 398–406 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    McFrederick, Q. S. et al. Flowers and wild megachilid bees share microbes. Microb. Ecol. 73, 188–200 (2017).PubMed 
    Article 

    Google Scholar 
    Jones, J. C. et al. The gut microbiome is associated with behavioural task in honey bees. Insectes Sociaux 65, 419–429 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kristensen, T. N., Schonherz, A., Rohde, P. D., Sorensen, J. G. & Loeschcke, V. Strong experimental support for the hologenome hypothesis revealed from Drosophila melanogaster selection lines. bioRxiv https://doi.org/10.1101/2021.09.09.459587 (2021)Bovo, S., Utzeri, V. J., Ribani, A., Cabbri, R. & Fontanesi, L. Shotgun sequencing of honey DNA can describe honey bee derived environmental signatures and the honey bee hologenome complexity. Sci. Rep. 10, 1–17 (2020).Article 
    CAS 

    Google Scholar 
    Dharampal, P. S., Carlson, C., Currie, C. R. & Steffan, S. A. Pollen-borne microbes shape bee fitness. Proc. R. Soc. B. 286, 20182894 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Graystock, P., Rehan, S. M. & McFrederick, Q. S. Hunting for healthy microbiomes: determining the core microbiomes of Ceratina, Megalopta, and Apis bees and how they associate with microbes in bee collected pollen. Conserv. Genet. 18, 701–711 (2017).Article 

    Google Scholar 
    Engel, P. et al. The bee microbiome: impact on bee health and model for evolution and ecology of host-microbe interactions. MBio 7, e02164–15 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Voulgari-Kokota, A., McFrederick, Q. S., Steffan-Dewenter, I. & Keller, A. Drivers, diversity, and functions of the solitary-bee microbiota. Trends Microbiol 27, 1034–1044 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rothman, J. A., Leger, L., Graystock, P., Russell, K. & McFrederick, Q. S. The bumble bee microbiome increases survival of bees exposed to selenate toxicity. Environ. Microbiol. 21, 3417–3429 (2019).CAS 
    Article 

    Google Scholar 
    Engel, P., Martinson, V. G. & Moran, N. A. Functional diversity within the simple gut microbiota of the honey bee. PNAS 109, 11002–11007 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Engel, P. & Moran, N. A. Functional and evolutionary insights into the simple yet specific gut microbiota of the honey bee from metagenomic analysis. Gut Microbes 4, 60–65 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kwong, W. K. et al. Dynamic microbiome evolution in social bees. Sci. Adv. 3, e1600513 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Breeze, T. D., Bailey, A. P., Balcombe, K. G. & Potts, S. G. Pollination services in the UK: How important are honeybees? Agric. Ecosyst. Environ. 142, 137–143 (2011).Article 

    Google Scholar 
    Dharampal, P. S., Hetherington, M. C. & Steffan, S. A. Microbes make the meal: oligolectic bees require microbes within their host pollen to thrive. Ecol. Entomol. 45, 1418–1427 (2020).Article 

    Google Scholar 
    Keller, A. et al. (More than) hitchhikers through the network: the shared microbiome of bees and flowers. Curr. Opin. Insect 44, 8–15 (2021).Article 

    Google Scholar 
    Hugenholtz, P. & Tyson, G. W. Metagenomics. Nature 455, 481–483 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Galbraith, D. A. et al. Investigating the viral ecology of global bee communities with high- throughput metagenomics. Sci. Rep. 8, 8879 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Regan, T. et al. Characterisation of the British honey bee metagenome. Nat. Commun. 9, 1–13 (2018).CAS 
    Article 

    Google Scholar 
    Bovo, S. et al. Shotgun metagenomics of honey DNA: Evaluation of a methodological approach to describe a multi-kingdom honey bee derived environmental DNA signature. PLOS ONE 13, e0205575 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Schoonvaere, K. et al. Unbiased RNA shotgun metagenomics in social and solitary wild bees detects associations with eukaryote parasites and new viruses. PLOS ONE 11, e0168456 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Cox-Foster, D. L. et al. A metagenomic survey of microbes in honey bee colony collapse disorder. Science 318, 283–287 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rehan, S. M., Leys, R. & Schwarz, M. P. A mid-cretaceous origin of sociality in xylocopine bees with only two origins of true worker castes. PLOS ONE 7, e34690 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rehan, S. M. Small carpenter bees (Ceratina). Encyclopedia of Social Insects (ed Chris, S.) (Springer, 2020).Sakagami, S. F. & Maeta, Y. Multifemale nests and rudimentary castes in the normally solitary bee Ceratina japonica (Hymenoptera: Xylocopinae). J. Kans. Entomol. 57, 639–656 (1984).
    Google Scholar 
    Huisken, J. L., Shell, W. A., Pare, H. K. & Rehan, S. M. The influence of social environment on cooperating and conflict in an incipiently social bee, Ceratina calcarata. Behav. Ecol. 75, 74 (2021).Article 

    Google Scholar 
    Rehan, S. M., Glastad, K. M., Lawson, S. P. & Hunt, B. G. The genome and methylome of a subsocial small carpenter bee, Ceratina calcarata. GBE 8, 1401–1410 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Rehan, S. M. et al. Conserved genes underlie phenotypic plasticity in an incipiently social bee. GBE 10, 2749–2758 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Arsenault, S. V., Hunt, B. G. & Rehan, S. M. The effect of maternal care on gene expression and DNA methylation in a subsocial bee. Nat. Commun. 9, 3468 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Shell, W. A. et al. Sociality sculpts similar patterns of molecular evolution in two independently evolved lineages of eusocial bees. Comms. Biol. 4, 1–9 (2021).Article 
    CAS 

    Google Scholar 
    Dew, R. M., McFrederick, Q. S. & Rehan, S. M. Diverse diets with consistent core microbiome in wild bee pollen provisions. Insects 11, 49 (2020).Article 

    Google Scholar 
    Lawson, S. P., Kennedy, K. & Rehan, S. M. Pollen composition significantly impacts development and survival of the native small carpenter bee, Ceratina calcarata. Ecol. Entomol. 46, 232–239 (2021).Article 

    Google Scholar 
    Oppenheimer, R. L., Shell, W. A. & Rehan, S. M. Phylogeography and population genetics of the Australian small carpenter bee, Ceratina australensis. Biol. J. Linn. Soc. 124, 747–755 (2018).Article 

    Google Scholar 
    McFrederick, Q. S. & Rehan, S. M. Wild bee pollen usage and microbial communities co- vary across landscapes. Microb. Ecol. 77, 513–522 (2018).PubMed 
    Article 
    CAS 

    Google Scholar 
    Rehan, S. M., Richards, M. H. & Schwarz, M. P. Sociality in the Australian small carpenter bee Ceratina (Neoceratina) australensis. Insectes Sociaux 57, 403–412 (2010).Article 

    Google Scholar 
    Harpur, B. A. & Rehan, S. M. Connecting social polymorphism to single nucleotide polymorphism: population genomics of the small carpenter bee, Ceratina australensis. Biol. J. Linn. Soc. 132, 945–954 (2021).Article 

    Google Scholar 
    Neu, A. T., Allen, E. E. & Roy, K. Defining and quantifying the core microbiome: challenges and prospects. PNAS 118, e2104429118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lawson, S. P., Ciaccio, K. N. & Rehan, S. M. Maternal manipulation of pollen provisions affects worker production in a small carpenter bee. Behav. Ecol. 70, 1891–1900 (2016).Article 

    Google Scholar 
    Ganeshprasad, D. N., Jani, K., Shouche, Y. S. & Sneharani, A. H. Gut bacterial inhabitants of open nested honey bee, Apis florea. Preprint at https://assets.researchsquare.com/files/rs-225332/v1/ddf21abe-2456-4f45-af61-4ba3e81d16e7.pdf?c=1641312753 (2021).Rothman, J. A., Cox-Foster, D. L., Andrikopoulos, C. & McFrederick, Q. S. Diet breadth affects bacterial identity but not diversity in the pollen provisions of closely related polylectic and oligolectic bees. Insects 11, 1–13 (2020).Article 

    Google Scholar 
    Cohen, H., McFrederick, Q. S. & Philpott, S. M. Environment shapes the microbiome of the blue orchard bee, Osmia lignaria. Microb. Ecol. 80, 897–907 (2020).PubMed 
    Article 

    Google Scholar 
    Dew, R. M., Rehan, S. M. & Schwarz, M. P. Biogeography and demography of an Australian native bee Ceratina australensis (Hymenoptera: Apidae) since the last glacial maximum. J. Hymenopt. Res. 49, 25–41 (2016).Article 

    Google Scholar 
    Pinto-Tomás, A. A. et al. Symbiotic nitrogen fixation in the fungus gardens of leaf-cutter ants. Science 326, 1120–1123 (2009).PubMed 
    Article 
    CAS 

    Google Scholar 
    Walterson, A. M. & Stavrinides, J. Pantoea insights into a highly versatile and diverse genus within the Enterobacteriaceae. FEMS Microbiol. Rev. 39, 968–984 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Scheiner, R., Strauß, S., Thamm, M., Farré-Armengol, G. & Junker, R. R. The bacterium Pantoea ananatis modifies behavioral responses to sugar solutions in honeybees. Insects 11, 692 (2020).PubMed Central 
    Article 

    Google Scholar 
    Leonhardt, S. D. & Kaltenpoth, M. Microbial communities of three sympatric Australian stingless bee species. Plos ONE 9, e105718 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Bailey, L. & Ball, B. V. Honey Bee Pathology (Academic Press, 1991).Tham, V. L. Isolation of Streptococcus pluton from the larvae of European honey bees in Australia. Aust. Vet. J. 54, 406–407 (1978).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bowman, J. The genus Flavobacterium. Prokaryotes 7, 481–531 (2006).
    Google Scholar 
    Voordouw, G. The genus Desulovibrio: The centennial. Appl. Environ. Microbiol. 61, 2813–2819 (1995).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Singaravelen, N., Nee’man, G., Inbar, M. & Izhaki, I. Feeding responses of free-flying honeybees to secondary compounds mimicking floral nectars. J. Chem. Ecol. 31, 2791–2804 (2005).Article 
    CAS 

    Google Scholar 
    Baracchi, D., Marples, A., Jenkins, A. J., Leitch, A. R. & Chittka, L. Nicotine in floral nectar pharmacologically influences bumblebee learning of floral features. Sci. Rep. 7, 1951 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Adler, L. S. & Irwin, R. E. Ecological costs and benefits of defenses in nectar. Ecology 86, 2968–2978 (2005).Article 

    Google Scholar 
    Bally, J. et al. Nicotiana paulineana, a new Australian species in Nicotiana section Suaveolentes. Aust. Syst. Bot. 34, 477–484 (2021).Article 

    Google Scholar 
    Coenye, T. & Vandamme, P. Diversity and significance of Burkholderia species occupying diverse ecology niches. Environ. Microbiol. 5, 719–729 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Levy, A., Merritt, A. J., Aravena-Roman, M., Hodge, M. M. & Inglis, T. J. J. Expanded range of Burkholderia species in Australia. Am. J. Trop. Med. Hyg. 78, 599–604 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kaltenpoth, M. & Flórez, L. V. Versatile and dynamic symbioses between insects and Burkholderia bacteria. Annu. Rev. Entomol. 65, 145–170 (2019).PubMed 
    Article 
    CAS 

    Google Scholar 
    Foley, K., Fazio, G., Jensen, A. B. & Hughes, W. O. H. Nutritional limitation and resistance to opportunistic Aspergillus parasites in honey bee larvae. J. Invertebr. Pathol. 111, 68–73 (2012).PubMed 
    Article 

    Google Scholar 
    Yoder, J. A. et al. Fungicide contamination reduces beneficial fungi in bee bread based on an area-wide field study in honey bee, Apis mellifera, colonies. J. Toxicol. Environ. Health Part A 76, 587–600 (2013).CAS 
    Article 

    Google Scholar 
    Yun, J.-H., Jung, M.-J., Kim, P. S. & Bae, J.-W. Social status shapes the bacterial and fungal gut communities of the honey bee. Sci. Rep. 8, 1–11 (2018).
    Google Scholar 
    Dew, R. M., Silva, D. P. & Rehan, S. M. Range expansion of an already widespread bee under climate change. GECCO 17, e00584 (2019).
    Google Scholar 
    Cambra, M., Capote, N. & Myrta, A. & Llácer, G. Plum pox virus and the estimated costs associated with sharka disease. EPPO Bull. 36, 202–204 (2006).Article 

    Google Scholar 
    Roberts, J. M. K., Ireland, K. B., Tay, W. T. & Paini, D. Honey bee-assisted surveillance for early plant virus detection. Ann. Appl. Biol. 173, 285–293 (2018).CAS 
    Article 

    Google Scholar 
    Elliott, B. et al. Pollen diets and niche overlap of honey bees and native bees in protected areas. BAAE 50, 169–180 (2021).
    Google Scholar 
    Porrini, C. et al. Use of honey bees as bioindicators of environmental pollution in Italy. in Honey bees: estimating the environmental impact of chemicals (eds Devillers, J. & Pham-Delegue, M.-H.) (Taylor & Francis Press, 2002).Kennedy, P., Higginson, A. D., Radford, A. N. & Sumner, S. Altruism in a volatile world. Nature 555, 359–362 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rubin, B. E. R., Sanders, J. G., Turner, K. M., Pierce, N. E. & Kocher, S. D. Social behaviour in bees influences the abundance of Sodalis (Enterobacteriaceae) symbionts. R. Soc. Open Sci. 5, 180369 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mohr, K. I. & Tebbe, C. C. Diversity and phylotype consistency of bacteria in the guts of three bee species (Apoidea) at an oilseed rape field. Environ. Microbiol. 8, 258–272 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Raymann, K. & Moran, N. A. The role of the gut microbiome in health and disease of adult honey bee workers. Curr. Opin. Insect Sci. 26, 97–104 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Amin, F. A. Z. et al. Probiotic properties of Bacillus strains isolated from stingless bee (Heterotrigona itama) honey collected across Malaysia. Int. J. Envrion. Res. Public Health 17, 1–15 (2020).
    Google Scholar 
    Takeshita, K. & Kikuchi, Y. Riptortus pedestris and Burkholderia symbiont: an ideal model system for insect-microbe symbiotic associations. Res. Microbiol. 168, 175–187 (2017).PubMed 
    Article 

    Google Scholar 
    Martinson, V. G. et al. A simple and distinctive microbiota associated with honey bees and bumble bees. Mol. Ecol. 20, 619–628 (2011).PubMed 
    Article 

    Google Scholar 
    D’Alvise, P. et al. The impact of winter feed type on intestinal microbiota and parasites in honey bees. Apidologie 49, 252–264 (2018).Article 
    CAS 

    Google Scholar 
    Wang, L. et al. Dynamic changes of gut microbial communities of bumble bee queens through important life stages. mSystems 4, e00631–19 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Kapheim, K. M., Johnson, M. M. & Jolley, M. Composition and acquisition of the microbiome in solitary, ground-nesting alkali bees. Sci. Rep. 11, 2993 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Abdelazez, A. et al. Potential benefits of Lactobacillus plantarum as probiotic and its advantages in human health and industrial applications: A review. Adv. Environ. Biol. 12, 16–27 (2018).CAS 

    Google Scholar 
    Frese, S. A. et al. The evolution of host specialization in the vertebrate gut symbiont Lactobacillus reuteri. PLoS Genet 7, e1001314 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Duar, R. M. et al. Lifestyles in transition: evolution and natural history of the genus Lactobacillus. FEMS Microbiol. Rev. 41, S27–S48 (2017).PubMed 
    Article 

    Google Scholar 
    Tejerina, M. R., Cabana, M. J. & Benitez-Ahrendts, M. R. Strains of Lactobacillus spp. reduce chalkbrood in Apis mellifera. J. Invertebr. Pathol. 178, 107521 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Vásquez, A. et al. Symbionts as major modulators of insect health: Lactic acid bacteria and honeybees. PLOS ONE 7, e33188 (2012).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Voulgari-Kokota, A., Steffan-Dewenter, I. & Keller, A. Susceptibility of red mason bee larvae to bacterial threats due to microbiome exchange with imported pollen provisions. Insects 11, 1–14 (2020).Article 

    Google Scholar 
    Steffan, S. A. et al. Omnivory in bees: Elevated trophic positions among all major bee families. Am. Nat. 194, 414–421 (2019).PubMed 
    Article 

    Google Scholar 
    Hurst, P. S. Social biology of Exoneurella tridentata, an allodapine bee with morphological castes and perennial colonies. Unpublished D. Phil. Thesis (Flinders University, 2001).Chalita, M. et al. Improved metagenomic taxonomic profiling using a curated core gene- based bacterial database reveals unrecognized species in the genus Streptococcus. Pathogens 9, 204 (2021).Article 

    Google Scholar 
    Rehan, S. M. & Toth, A. L. Climbing the social ladder: molecular evolution of sociality. Trends Ecol. Evol. 30, 426–433 (2015).PubMed 
    Article 

    Google Scholar 
    Shell, W. A. & Rehan, S. M. Behavioral and genetic mechanisms of social evolution: insights from incipiently and facultatively social bees. Apidologie 49, 13–30 (2018).CAS 
    Article 

    Google Scholar 
    Kirby, K. S. Isolation and characterization of ribosomal ribonucleic acid. Biochem. J. 96, 266–269 (1956).Article 

    Google Scholar 
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2019).Article 
    CAS 

    Google Scholar 
    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Tsilimigras, M. C. B. & Fodor, A. A. Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Ann. Epidemiol. 26, 330–335 (2016).PubMed 
    Article 

    Google Scholar 
    Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res 27, 824–834 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Altschul, S. F. et al. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
    PubMed 
    Article 

    Google Scholar 
    Oksanen, J. et al. Package ‘vegan’. Community Ecology package, version 2, 1–295 (2013).Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
    Google Scholar 
    Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mina, R., Haixu, T. & Yuzhen, Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 38, e191 (2010).Article 
    CAS 

    Google Scholar 
    Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).Article 
    CAS 

    Google Scholar 
    Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinf 9, 599 (2008).Article 
    CAS 

    Google Scholar 
    Langfelder, P. & Horvath, S. Tutorials for the WGCNA package. https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/ (2016).Liaw, A. & Wiener, M. Classification and regression by randomForest. R. N. 2, 18–22 (2002).
    Google Scholar 
    Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).Article 

    Google Scholar 
    Paluszynska, A. Structure mining and knowledge extraction from random forest with applications to The Cancer Genome Atlas project. Master’s Thesis (University of Warsaw, 2017). More

  • in

    Applying convolutional neural networks to speed up environmental DNA annotation in a highly diverse ecosystem

    Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G., Davis, A., Dean, J., Devin, M., Ghemawat, S. & Zheng, X. TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org. (2015).Alberdi, A., Aizpurua, O., Gilbert, M. T. P. & Bohmann, K. Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods Ecol. Evol. 9, 134–147 (2018).Article 

    Google Scholar 
    Albert, J. S. & Reis, R. E. One. Introduction to Neotropical freshwaters. In Historical biogeography of Neotropical freshwater fishes (pp. 3-20). University of California Press. (2011).Allard, L., Popée, M., Vigouroux, R. & Brosse, S. Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages. Aquat. Sci. 78, 315–325 (2016).Article 

    Google Scholar 
    Berry, O. et al. Making environmental DNA (eDNA) biodiversity records globally accessible. Environ. DNA 3(4), 699–705 (2020).Article 

    Google Scholar 
    Bohmann, K. et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol. 29(6), 358–367 (2014).PubMed 
    Article 

    Google Scholar 
    Bolyen, E. et al. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. Nat. Biotechnol. 32, 852–857 (2019).Article 
    CAS 

    Google Scholar 
    Bonder, M. J., Abeln, S., Zaura, E. & Brandt, B. W. Comparing clustering and pre-processing in taxonomy analysis. Bioinformatics 28(22), 2891–2897 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Boussarie, G. et al. Environmental DNA illuminates the dark diversity of sharks. Sci. Adv. 4, eaap9661 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Boyer, F. et al. obitools: A unix-inspired software package for DNA metabarcoding. Mol. Ecology Resour. 16(1), 176–182 (2016).CAS 
    Article 

    Google Scholar 
    Brandt, M.I., Trouche, B., Quintric, L., Günther, B., Wincker, P., Poulain, J. & Arnaud-Haond, S. Bioinformatic pipelines combining denoising and clustering tools allow for more comprehensive prokaryotic and eukaryotic metabarcoding. Molecular Ecology Resources. Accepted (2021).Brosse, S., Melki, F. & Vigouroux, R. Fishes from the Mitaraka mountains (French Guiana). Zoosystema 41, 131–151 (2019).Article 

    Google Scholar 
    Brown, E. A., Chain, F. J., Crease, T. J., MacIsaac, H. J. & Cristescu, M. E. Divergence thresholds and divergent biodiversity estimates: can metabarcoding reliably describe zooplankton communities?. Ecol. Evol. 5(11), 2234–2251 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Busia, K., George, D. E., Fannjiang, C., Alexander, D.H., Dorfman, E., Poplin, R., Chang, P., & DePris, M. A deep learning approach to pattern recognition for short DNA sequences. BioRxiv (2020).Bylemans, J., Gleeson, D. M., Hardy, C. M. & Furlan, E. Toward an ecoregion scale evaluation of eDNA metabarcoding primers: A case study for the freshwater fish biodiversity of the Murray-Darling Basin (Australia). Ecol. Evol. 8(17), 8697–8712 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Calderón-Sanou, I., Münkemüller, T., Boyer, F., Zinger, L. & Thuiller, W. From environmental DNA sequences to ecological conclusions: How strong is the influence of methodological choices?. J. Biogeogr. 47(1), 193–206 (2020).Article 

    Google Scholar 
    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13(7), 581–583 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cantera, I., Coutant, O., Jézéuel, C., Decotte, J.B., Dejean, T., Vigouroux, R., Valentini, A. Murienne, J. & Brosse S. Slight deforestation causes harsh biodiversity decline in Amazonian rivers (submitted)Cantera, I., Decotte, J. B., Dejean, T., Murienne, J., Vigouroux, R., Valentini, A., & Brosse, S. Characterizing the spatial signal of environmental DNA in river systems using a community ecology approach. BioRxiv (2020).Cantera, I. et al. Optimizing environmental DNA sampling effort for fish inventories in tropical streams and rivers. Sci. Rep. 9(1), 1–1 (2019).CAS 
    Article 

    Google Scholar 
    Cardoso, Y. P. & Montoya-Burgos, J. I. Unexpected diversity in the catfish Pseudancistrus brevispinis reveals dispersal routes in a Neotropical center of endemism: The Guyanas Region. Mol. Ecol. 18, 947–964 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cilleros, K. et al. Unlocking biodiversity and conservation studies in high-diversity environments using environmental DNA (eDNA): A test with Guianese freshwater fishes. Mol. Ecol. Resour. 19(1), 27–46 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Collen, B., Ram, M., Zamin, T. & McRae, L. The tropical biodiversity data gap: Addressing disparity in global monitoring. Trop. Conserv. Sci. 1(2), 75–88 (2008).Article 

    Google Scholar 
    Cordier, T., Lanzén, A., Apothéloz-Perret-Gentil, L., Stoeck, T. & Pawlowski, J. Embracing environmental genomics and machine learning for routine biomonitoring. Trends Microbiol. 27(5), 387–397 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cordier, T. et al. Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap. Mol. Ecol. 30(13), 2937–2958 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Coutant, O. et al. Detecting fish assemblages with environmental DNA: Does protocol matter? Testing eDNA metabarcoding method robustness. Environ. DNA 3(3), 619–630 (2020).Article 

    Google Scholar 
    Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26(21), 5872–5895 (2017).PubMed 
    Article 

    Google Scholar 
    Deneu, B., Servajean, M., Bonnet, P., Botella, C., Munoz, F., & Joly, A. Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLoS Comput. Biol. (in press) (2021).de Mérona, B., Tejerina-Garro, F. L. & Vigouroux, R. Fish-habitat relationships in French Guiana rivers: A review. Cybium 36, 7–15 (2012).
    Google Scholar 
    DiBattista, J. D. et al. Environmental DNA can act as a biodiversity barometer of anthropogenic pressures in coastal ecosystems. Sci. Rep. 10(1), 1–15 (2020).Article 
    CAS 

    Google Scholar 
    Dornelas, M., Madin, E. M., Bunce, M., DiBattista, J. D., Johnson, M., Madin, J. S., Magurran, A. E., McGill, B. J., Pettorelli, N., Pizarro, O. & Williams, S. B. Towards a macroscope: Leveraging technology to transform the breadth, scale and resolution of macroecological data. Glob. Ecol. Biogeogr. (2019).Dufresne, Y., Lejzerowicz, F., Perret-Gentil, L. A., Pawlowski, J. & Cordier, T. SLIM: A flexible web application for the reproducible processing of environmental DNA metabarcoding data. BMC Bioinform. 20(1), 1–6 (2019).Article 

    Google Scholar 
    Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 4(4), 423–425 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ficetola, G. F., Taberlet, P. & Coissac, E. How to limit false positives in environmental DNA and metabarcoding?. Mol. Ecol. Resour. 16(3), 604–607 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ficetola, G. F. et al. Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data. Mol. Ecology Resour. 15(3), 543–556 (2015).CAS 
    Article 

    Google Scholar 
    Flynn, J. M., Brown, E. A., Chain, F. J., MacIsaac, H. J. & Cristescu, M. E. Toward accurate molecular identification of species in complex environmental samples: Testing the performance of sequence filtering and clustering methods. Ecol. Evol. 5(11), 2252–2266 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gold, Z. et al. eDNA metabarcoding bioassessment of endangered fairy shrimp (Branchinecta spp.). Conserv. Genet. Resour. 12, 685–690 (2020).Article 

    Google Scholar 
    Grünig, M., Razavi, E., Calanca, P., Mazzi, D., Wegner, J. D., & Pellissier, L. Applying deep neural networks to predict incidence and phenology of plant pests and diseases. Ecosphere (accepted) (2021).Helaly, M. A., Rady, S., & Aref, M. M. Convolutional neural networks for biological sequence taxonomic classification: A comparative study. In International Conference on Advanced Intelligent Systems and Informatics (pp. 523–533). Springer, Cham (2019).Holman, L. E. et al. Animals, protists and bacteria share marine biogeographic patterns. Nat. Ecol. Evol. 5(6), 738–746 (2021).PubMed 
    Article 

    Google Scholar 
    Iknayan, K. J., Tingley, M. W., Furnas, B. J. & Beissinger, S. R. Detecting diversity: Emerging methods to estimate species diversity. Trends Ecol. Evol. 29(2), 97–106 (2014).PubMed 
    Article 

    Google Scholar 
    Jarman, S. N., Berry, O. & Bunce, M. The value of environmental DNA biobanking for long-term biomonitoring. Nat. Ecol. Evol. 2(8), 1192–1193 (2018).PubMed 
    Article 

    Google Scholar 
    Juhel, J. B., Utama, R. S., Marques, V., Vimono, I. B., Sugeha, H. Y., Kadarusman, Pouyaud, L., Dejean, T., Mouillot, D. & Hocdé, R. Accumulation curves of environmental DNA sequences predict coastal fish diversity in the coral triangle. Proc. R. Soc. B 287(1930), 20200248 (2020).Kopp, W., Monti, R., Tamburrini, A., Ohler, U. & Akalin, A. Deep learning for genomics using Janggu. Nat. Commun. 11(1), 1–7 (2020).Article 
    CAS 

    Google Scholar 
    Le Bail, P. Y. et al. Updated checklist of the freshwater and estuarine fishes of French Guiana. Cybium 36(1), 293–319 (2012).
    Google Scholar 
    LeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989).Article 

    Google Scholar 
    Li, W. et al. Validating eDNA measurements of the richness and abundance of anurans at a large scale. J. Anim. Ecol. 90(6), 1466–1479 (2021).PubMed 
    Article 

    Google Scholar 
    Lopes, C. M. et al. eDNA metabarcoding: A promising method for anuran surveys in highly diverse tropical forests. Mol. Ecol. Resour. 17(5), 904–914 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Makiola, A. et al. Key questions for next-generation biomonitoring. Front. Environ. Sci. 7, 197 (2020).Article 

    Google Scholar 
    Marques, V. et al. Blind assessment of vertebrate taxonomic diversity across spatial scales by clustering environmental DNA metabarcoding sequences. Ecography 43(12), 1779–1790 (2020).Article 

    Google Scholar 
    Marques, V. et al. GAPeDNA: Assessing and mapping global species gaps in genetic databases for eDNA metabarcoding. Divers. Distrib. 27(10), 1880–1892 (2020).Article 

    Google Scholar 
    Mathon, L. et al. Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification. Mol. Ecol. Resour. 21(7), 2565–2579 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    McGee, K. M., Robinson, C. & Hajibabaei, M. Gaps in DNA-based biomonitoring across the globe. Front. Ecol. Evol. 7, 337 (2019).Article 

    Google Scholar 
    Murienne, J. et al. Aquatic eDNA for monitoring French Guiana biodiversity. Biodivers. Data J. 7, e37518 (2019).Nugent, C. M. & Adamowicz, S. J. Alignment-free classification of COI DNA barcode data with the Python package Alfie. Metabarcoding Metagenomics 4, e55815 (2020).Pagni, M. et al. Density-based hierarchical clustering of pyro-sequences on a large scale-the case of fungal ITS1. Bioinformatics 29(10), 1268–1274 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Papa, Y., Le Bail, P. Y. & Covain, R. Genetic landscape clustering of a large DNA barcoding dataset reveals shared patterns of genetic divergence among freshwater fishes of the Maroni Basin. Authorea Preprints (2020).Piro, V. C., Dadi, T. H., Seiler, E., Reinert, K. & Renard, B. Y. ganon: Precise metagenomics classification against large and up-to-date sets of reference sequences. Bioinformatics 36(Supplement 1), i12–i20 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Polanco Fernández, A., Marques, V., Fopp, F., Juhel, J. B., Borrero-Pérez, G. H., Cheutin, M. C., Eme, D. & Pellissier, L. Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes. Environ. DNA 3, 142–156 (2021).Polanco, A. et al. Comparing the performance of 12S mitochondrial primers for fish environmental DNA across ecosystems. Environ. DNA 3(6), 1113–1127 (2021).Article 

    Google Scholar 
    Polanco Fernández, A., Martinezguerra, M. M., Marques, V., Francisco Villa-Navarro, Borrero-Pérez, G. H., Cheutin, M. C., Dejean, T., Hocdé, R., Juhel, J. B., Maire, E., Manel, S. & Pellissier, L. Recovering aquatic and terrestrial biodiversity in a tropical estuary using environmental DNA. Biotropica 53(6), 1606–1619 (2021).Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, 1–22 (2016).Article 

    Google Scholar 
    Rojahn, J., Gleeson, D. M., Furlan, E., Haeusler, T. & Bylemans, J. Improving the detection of rare native fish species in environmental DNA metabarcoding surveys. Aquat. Conserv. Mar. Freshw. Ecosyst. 31(4), 990–997 (2021).Article 

    Google Scholar 
    Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 17, e00547 (2019).Article 

    Google Scholar 
    Sato, Y., Miya, M., Fukunaga, T., Sado, T. & Iwasaki, W. MitoFish and MiFish pipeline: A mitochondrial genome database of fish with an analysis pipeline for environmental DNA metabarcoding. Mol. Biol. Evol. 35(6), 1553–1555 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schirmer, M. et al. Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Res. 43(6), e37 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Schnell, I. B., Bohmann, K. & Gilbert, M. T. P. Tag jumps illuminated–reducing sequence-to-sample misidentifications in metabarcoding studies. Mol. Ecol. Resour. 15(6), 1289–1303 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sepulveda, A. J., Nelson, N. M., Jerde, C. L. & Luikart, G. Are environmental DNA methods ready for aquatic invasive species management?. Trends Ecol. Evol. 35, 668–678 (2020).PubMed 
    Article 

    Google Scholar 
    Shokralla, S., Spall, J. L., Gibson, J. F. & Hajibabaei, M. Next-generation sequencing technologies for environmental DNA research. Mol. Ecol. 21(8), 1794–1805 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Shorten, C. & Khoshgoftaar, T. A survey on image data augmentation for deep learning. J. Big Data 6, 60 (2019).Article 

    Google Scholar 
    Singer, G. A. C., Fahner, N. A., Barnes, J. G., McCarthy, A. & Hajibabaei, M. Comprehensive biodiversity analysis via ultra-deep patterned flow cell technology: A case study of eDNA metabarcoding seawater. Sci. Rep. 9(1), 1–12 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Su, G. et al. Human impacts on global freshwater fish biodiversity. Science 371(6531), 835 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Taberlet, P., Bonin, A., Coissac, E. & Zinger, L. Environmental DNA: For Biodiversity Research and Monitoring (Oxford University Press, Oxford, 2018).Book 

    Google Scholar 
    Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21(8), 2045–2050 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Thomsen, P. F. & Willerslev, E. Environmental DNA-An emerging tool in conservation for monitoring past and present biodiversity. Biol. Conserv. 183, 4–18 (2015).Article 

    Google Scholar 
    Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD–A platform for ensemble forecasting of species distributions. Ecography 32(3), 369–373 (2009).Article 

    Google Scholar 
    Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25(4), 929–942 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    West, K. et al. Large-scale eDNA metabarcoding survey reveals marine biogeographic break and transitions over tropical north-western Australia. Divers. Distrib. 27(10), 1942–1957 (2021).Article 

    Google Scholar  More

  • in

    Characterization of triatomine bloodmeal sources using direct Sanger sequencing and amplicon deep sequencing methods

    Blosser, E. M. et al. Environmental drivers of seasonal patterns of host utilization by Culiseta melanura (Diptera: Culicidae) in Florida. J. Med. Entomol. 54, 1365–1374 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Burkett-Cadena, N. D., Hassan, H. K., Eubanks, M. D., Cupp, E. W. & Unnasch, T. R. Winter severity predicts the timing of host shifts in the mosquito Culex erraticus. Biol. Lett. 8, 567–569 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Gürtler, R. E., Cecere, M. C., Vazquez, D. P., Chuit, R. & Cohen, J. E. Host-feeding patterns of domiciliary Triatoma infestans (Hemiptera: Reduviidae) in northwest Argentina: Seasonal and instar variation. J. Med. Entomol. 33, 15–26 (1996).PubMed 

    Google Scholar 
    Rabinovich, J. E. et al. Ecological patterns of blood-feeding by kissing-bugs (Hemiptera: Reduviidae: Triatominae). Mem. Inst. Oswaldo Cruz 106, 479–494 (2011).PubMed 

    Google Scholar 
    Kent, R. J. Molecular methods for arthropod bloodmeal identification and applications to ecological and vector-borne disease studies. Mol. Ecol. Resour. 9, 4–18 (2009).CAS 
    PubMed 

    Google Scholar 
    Cecere, M. C. et al. Host-feeding sources and infection with Trypanosoma cruzi of Triatoma infestans and Triatoma eratyrusiformis (Hemiptera: Reduviidae) from the Calchaqui Valleys in Northwestern Argentina. J. Med. Entomol. 53, 666–673 (2016).CAS 
    PubMed 

    Google Scholar 
    Logue, K. et al. Unbiased characterization of Anopheles mosquito blood meals by targeted high-throughput sequencing. PLoS Negl. Trop. Dis. 10, e0004512 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Keller, J. I., Schmidt, J. O., Schmoker, A. M., Ballif, B. A. & Stevens, L. Protein mass spectrometry extends temporal blood meal detection over polymerase chain reaction in mouse-fed Chagas disease vectors. Mem. Inst. Oswaldo Cruz 113, e180160 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Borland, E. M. & Kading, R. C. Modernizing the toolkit for arthropod bloodmeal identification. Insects 12, 1–27 (2021).
    Google Scholar 
    Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. & Sayers, E. W. GenBank. Nucleic Acids Res. 44, D67–D72 (2016).CAS 
    PubMed 

    Google Scholar 
    Hamer, S. A. et al. Comparison of DNA and carbon and nitrogen stable isotope-based techniques for identification of prior vertebrate hosts of ticks. J. Med. Entomol. 52, 1043–1049 (2015).CAS 
    PubMed 

    Google Scholar 
    Scott, M. C., Harmon, J. R., Tsao, J. I., Jones, C. J. & Hickling, G. J. Reverse line blot probe design and polymerase chain reaction optimization for bloodmeal analysis of ticks from the eastern United States. J. Med. Entomol. 49, 697–709 (2012).CAS 
    PubMed 

    Google Scholar 
    Arias-Giraldo, L. M. et al. Identification of blood-feeding sources in Panstrongylus, Psammolestes, Rhodnius and Triatoma using amplicon-based next-generation sequencing. Parasit. Vectors 13, 1–14 (2020).
    Google Scholar 
    Kieran, T. J. et al. Blood meal source characterization using Illumina sequencing in the Chagas Disease vector Rhodnius pallescens (Hemiptera: Reduviidae) in Panamá. J. Med. Entomol. https://doi.org/10.1093/jme/tjx170 (2017).Article 
    PubMed 

    Google Scholar 
    Dumonteil, E. et al. Detailed ecological associations of triatomines revealed by metabarcoding and next-generation sequencing: Implications for triatomine behavior and Trypanosoma cruzi transmission cycles. Sci. Rep. 8, 1–13 (2018).CAS 

    Google Scholar 
    Estrada-Franco, J. G. et al. Vertebrate-Aedes aegypti and Culex quinquefasciatus (Diptera)-arbovirus transmission networks: Non-human feeding revealed by meta-barcoding and nextgeneration sequencing. PLoS Negl. Trop. Dis. 14, 1–22 (2020).
    Google Scholar 
    Campana, M. G. et al. Simultaneous identification of host, ectoparasite and pathogen DNA via in-solution capture. Mol. Ecol. Resour. 16, 1224–1239 (2016).CAS 
    PubMed 

    Google Scholar 
    Klotz, S. A. et al. Free-roaming kissing bugs, vectors of Chagas disease, feed often on humans in the Southwest. Am. J. Med. 127, 421–426 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Waleckx, E., Suarez, J., Richards, B. & Dorn, P. L. Triatoma sanguisuga blood meals and potential for Chagas Disease, Louisiana, USA. Emerg. Infect. Dis. 20, 2141–2143 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kjos, S. A. et al. Identification of bloodmeal sources and Trypanosoma cruzi infection in triatomine bugs (Hemiptera: Reduviidae) from residential settings in Texas, the United States. J. Med. Entomol. 50, 1126–1139 (2013).PubMed 

    Google Scholar 
    Gürtler, R. E., Cohen, J. E., Cecere, M. C. & Chuit, R. Shifting host choices of the vector of Chagas Disease, Triatoma Infestans, in relation to the availability of host in houses in North-West Argentina. J. Appl. Ecol. 34, 699–715 (1997).
    Google Scholar 
    Minuzzi-Souza, T. et al. Molecular bloodmeal analyses reveal that Trypanosoma cruzi-infected, native triatomine bugs often feed on humans in houses in central Brazil. Med. Vet. Entomol. 32, 504–508 (2018).CAS 
    PubMed 

    Google Scholar 
    Lent, H. & Wygodzinsky, P. W. Revision of the Triatominae (Hemiptera, Reduviidae), and their significance as vectors of Chagas’ Disease. Bull. Am. Museum Nat. Hist. 163, 123–520 (1979).
    Google Scholar 
    World Health Organization. Chagas disease in Latin America: An epidemiological update based on 2010 estimates. Wkly. Epidemiol. Rec. 6, 33–44 (2015).
    Google Scholar 
    Dorn, P. L. et al. Autochthonous transmission of Trypanosoma cruzi, Louisiana. Emerg. Infect. Dis. 13, 13–15 (2007).
    Google Scholar 
    Cantey, P. T. et al. The United States Trypanosoma cruzi infection study: Evidence for vector-borne transmission of the parasite that causes Chagas disease among United States blood donors. Transfusion 52, 1922–1930 (2012).PubMed 

    Google Scholar 
    Garcia, M. N. et al. Molecular identification and genotyping of Trypanosoma cruzi DNA in autochthonous Chagas disease patients from Texas, USA. Infect. Genet. Evol. 49, 151–156 (2017).CAS 
    PubMed 

    Google Scholar 
    Barr, S., Gossett, K. A. & Klei, T. R. Clinical, clinicopathologic, and parasitologic observations of trypanosomiasis in dogs infected with North American Trypanosoma cruzi isolates. Am. J. Vet. Res. 52, 954–960 (1991).CAS 
    PubMed 

    Google Scholar 
    Meyers, A. C., Meinders, M. & Hamer, S. A. Widespread Trypanosoma cruzi infection in government working dogs along the Texas-Mexico border: Discordant serology, parasite genotyping and associated vectors. PLoS Negl. Trop. Dis. 11, 1–19 (2017).
    Google Scholar 
    Meyers, A. C., Edwards, E. E., Sanders, J. P., Saunders, A. B. & Hamer, S. A. Fatal Chagas myocarditis in government working dogs in the southern United States: Cross-reactivity and differential diagnoses in five cases across six months. Vet. Parasitol. Reg. Stud. Rep. 24, 1–7 (2021).
    Google Scholar 
    Hodo, C. L. & Hamer, S. A. Toward an ecological framework for assessing reservoirs of vector-borne pathogens: Wildlife reservoirs of Trypanosoma cruzi across the southern United States. ILAR J. 58, 379–392 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Guarneri, A. A., Pereira, M. H. & Diotaiuti, L. Influence of the blood meal source on the development of Triatoma infestans, Triatoma brasiliensis, Triatoma sordida, and Triatoma pseudomaculata (Heteroptera, Reduviidae). J. Med. Entomol. 37, 373–379 (2000).CAS 
    PubMed 

    Google Scholar 
    Pippin, W. F. The biology and vector capability of Triatoma sanguisuga texana Usinger and Triatoma gerstaeckeri (Stål) compared with Rhodnius prolixus (Stål) (Hemiptera: Triatominae). J. Med. Entomol. 7, 30–45 (1970).CAS 
    PubMed 

    Google Scholar 
    Bern, C., Kjos, S., Yabsley, M. J. & Montgomery, S. P. Trypanosoma cruzi and Chagas’ disease in the United States. Clin. Microbiol. Rev. 24, 655–681 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Kjos, S. et al. Distribution and characterization of canine Chagas disease in Texas. Vet. Parasitol. 152, 249–256 (2008).CAS 
    PubMed 

    Google Scholar 
    Tenney, T. D., Curtis-Robles, R., Snowden, K. F. & Hamer, S. A. Shelter dogs as sentinels for Trypanosoma cruzi transmission across Texas. Emerg. Infect. Dis. 20, 1323–1326 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Curtis-Robles, R., Wozniak, E. J., Auckland, L. D., Hamer, G. L. & Hamer, S. A. Combining public health education and disease ecology research: Using citizen science to assess Chagas disease entomological risk in Texas. PLoS Negl. Trop. Dis. 9, e0004235 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Curtis-Robles, R., Hamer, S. A., Lane, S., Levy, M. Z. & Hamer, G. L. Bionomics and spatial distribution of triatomine vectors of Trypanosoma cruzi in Texas and other southern states, USA. Am. J. Trop. Med. Hyg. 98, 113–121 (2018).PubMed 

    Google Scholar 
    Curtis-Robles, R., Aukland, L. D., Snowden, K. F., Hamer, G. L. & Hamer, S. A. Analysis of over 1500 triatomine vectors from across the US, predominantly Texas, for Trypanosoma cruzi infection and discrete typing units. Infect. Genet. Evol. 58, 171–180 (2018).PubMed 

    Google Scholar 
    Hodo, C. L., Wilkerson, G. K., Birkner, E. C., Gray, S. B. & Hamer, S. A. Trypanosoma cruzi transmission among captive nonhuman primates, wildlife, and vectors. EcoHealth 15, 426–436 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Duffy, T. et al. Analytical performance of a multiplex Real-Time PCR assay using TaqMan probes for quantification of Trypanosoma cruzi satellite DNA in blood samples. PLoS Negl. Trop. Dis. 7, e2000 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Piron, M. et al. Development of a real-time PCR assay for Trypanosoma cruzi detection in blood samples. Acta Trop. 103, 195–200 (2007).CAS 
    PubMed 

    Google Scholar 
    Curtis-Robles, R. et al. Parasitic interactions among Trypanosoma cruzi, triatomine vectors, domestic animals, and wildlife in Big Bend National Park along the Texas-Mexico border. Acta Trop. 188, 225–233 (2018).PubMed 

    Google Scholar 
    Cupp, E. W. et al. Identification of reptilian and amphibian blood meals from mosquitoes in an eastern equine encephalomyelitis virus focus in central Alabama. Am. J. Trop. Med. Hyg. 71, 272–276 (2004).PubMed 

    Google Scholar 
    Medeiros, M. C. I., Ricklefs, R. E., Brawn, J. D. & Hamer, G. L. Plasmodium prevalence across avian host species is positively associated with exposure to mosquito vectors. Parasitology 142, 1612–1620 (2015).CAS 
    PubMed 

    Google Scholar 
    Hamer, G. L. et al. Host selection by Culex pipiens mosquitoes and West Nile virus amplification. Am. J. Trop. Med. Hyg. 80, 268–278 (2009).PubMed 

    Google Scholar 
    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
    PubMed 

    Google Scholar 
    Hathaway, N. J., Parobek, C. M., Juliano, J. J. & Bailey, J. A. SeekDeep: Single-base resolution de novo clustering for amplicon deep sequencing. Nucleic Acids Res. 46, e21 (2018).CAS 
    PubMed 

    Google Scholar 
    Zeledón, R. et al. An Appraisal of the Status of Chagas Disease in the United States (Elsevier Inc., Amsterdam, 2012).
    Google Scholar 
    Gorchakov, R. et al. Trypanosoma cruzi infection prevalence and bloodmeal analysis in triatomine vectors of Chagas disease from rural peridomestic locations in Texas, 2013–2014. J. Med. Entomol. 53, 911–918 (2016).CAS 
    PubMed 

    Google Scholar 
    Stevens, L. et al. Vector blood meals and Chagas Disease transmission potential, United States. Emerg. Infect. Dis. 18, 646–650 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Polonio, R., López-Domínguez, J., Herrera, C. & Dumonteil, E. Molecular ecology of Triatoma dimidiata in southern Belize reveals risk for human infection and the local differentiation of Trypanosoma cruzi parasites. Int. J. Infect. Dis. 108, 320–329 (2021).CAS 
    PubMed 

    Google Scholar 
    Sasaki, H., Rosales, R. & Tabaru, Y. Host feeding profiles of Rhodnius prolixus and Triatoma dimidiata in Guatemala (Hemiptera: Reduviidae: Triatominae). Med. Entomol. Zool. 54, 283–289 (2003).
    Google Scholar 
    Villalobos, G., Martínez-Hernández, F., de la Torre, P., Laclette, J. P. & Espinoza, B. Entomological indices, feeding sources, and molecular identification of Triatoma phyllosoma (Hemiptera: Reduviidae) one of the main vectors of Chagas disease in the Istmo de Tehuantepec, Oaxaca, Mexico. Am. J. Trop. Med. Hyg. 85, 490–497 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Mota, J. et al. Identification of blood meal source and infection with Trypanosoma cruzi of Chagas disease vectors using a multiplex cytochrome b polymerase chain reaction assay. Vector Borne Zoonotic Dis. 7, 617–627 (2007).PubMed 

    Google Scholar 
    Pizarro, J. C. & Stevens, L. A new method for forensic DNA analysis of the blood meal in Chagas disease vectors demonstrated using Triatoma infestans from Chuquisaca, Bolivia. PLoS ONE 3, e3585 (2008).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Abad-Franch, F. & Gurgel-Gonçalves, R. The ecology and natural history of wild triatominae in the Americas. In Triatominae—The Biology of Chagas Disease Vectors (eds Guarneri, A. & Lorenzo, M.) 387–445 (Springer Nature Switzerland AG, 2021). https://doi.org/10.1007/978-3-030-64548-9_16.Chapter 

    Google Scholar 
    Busselman, R. E. & Hamer, S. A. Chagas disease ecology in the United States: Recent advances in understanding Trypanosoma cruzi transmission among triatomines, wildlife, and domestic animals and a quantitative synthesis of vector-host interactions. Annu. Rev. Anim. Biosci. 10, 325–348 (2022).PubMed 

    Google Scholar 
    Minuzzi-Souza, T. T. C. et al. Vector-borne transmission of Trypanosoma cruzi among captive Neotropical primates in a Brazilian zoo. Parasit. Vectors 9, 1–7. https://doi.org/10.1186/s13071-016-1334-7 (2016).CAS 
    Article 

    Google Scholar 
    Reis, F. C. et al. Trypanosomatid infections in captive wild mammals and potential vectors at the Brasilia Zoo, Federal District, Brazil. Vet. Med. Sci. 6, 248–256. https://doi.org/10.1002/vms3.216 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Martínez-Hernández, F., Oria-Martínez, B., Rendón-Franco, E., Villalobos, G. & Muñoz-García, C. I. Trypanosoma cruzi, beyond the dogma of non-infection in birds. Infect. Genet. Evol. https://doi.org/10.1016/j.meegid.2022.105239 (2022).Article 
    PubMed 

    Google Scholar 
    Botto-Mahan, C. et al. Lizards as silent hosts of Trypanosoma cruzi. Emerg. Infect. Dis. 28, 1250–1253. https://doi.org/10.3201/eid2806.220079 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

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    PISCOeo_pm, a reference evapotranspiration gridded database based on FAO Penman-Monteith in Peru

    Allen, R. G. et al. Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. FAO, Rome 300, D05109 http://www.fao.org/docrep/X0490E/X0490E00.htm (1998).
    Google Scholar 
    Trenberth, K. E., Fasullo, J. T. & Kiehl, J. Earth’s global energy budget. Bulletin of the American Meteorological Society 90, 311–324, https://doi.org/10.1175/2008BAMS2634.1 (2009).ADS 
    Article 

    Google Scholar 
    Wang, K. & Dickinson, R. E. A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Reviews of Geophysics 50, https://doi.org/10.1029/2011RG000373 (2012).Liu, W. Evaluating remotely sensed monthly evapotranspiration against water balance estimates at basin scale in the Tibetan Plateau. Hydrology Research 49, 1977–1990, https://doi.org/10.2166/nh.2018.008 (2018).Article 

    Google Scholar 
    Valipour, M., Bateni, S. M., Gholami Sefidkouhi, M. A., Raeini-Sarjaz, M. & Singh, V. P. Complexity of forces driving trend of reference evapotranspiration and signals of climate change. Atmosphere 11, 1081, https://doi.org/10.1007/s41748-021-00252-3 (2020).ADS 
    Article 

    Google Scholar 
    Anwar, S. A., Mamadou, O., Diallo, I. & Sylla, M. B. On the influence of vegetation cover changes and vegetation-runoff systems on the simulated summer potential evapotranspiration of Tropical Africa using RegCM4. Earth Systems and Environment 5, 883–897, https://doi.org/10.3390/atmos11101081 (2021).ADS 
    Article 

    Google Scholar 
    Tomas-Burguera, M., Vicente-Serrano, S. M., Beguera, S., Reig, F. & Latorre, B. Reference crop evapotranspiration database in Spain (1961–2014). Earth System Science Data 11, 1917–1930, https://doi.org/10.5194/essd-11-1917-2019 (2019).ADS 
    Article 

    Google Scholar 
    Córdova, M., Carrillo-Rojas, G., Crespo, P., Wilcox, B. & Célleri, R. Evaluation of the Penman-Monteith (FAO 56 PM) Method for Calculating Reference Evapotranspiration Using Limited Data. Mountain Research and Development 35, 230–239, https://doi.org/10.1659/MRD-JOURNAL-D-14-0024.1 (2015).Article 

    Google Scholar 
    Valle Júnior, L. C. G. d. et al. Evaluation of FAO-56 procedures for estimating reference evapotranspiration using missing climatic data for a Brazilian tropical savanna. Water 13, 1763, https://doi.org/10.3390/w13131763 (2021).Article 

    Google Scholar 
    Djaman, K., Irmak, S. & Futakuchi, K. Daily reference evapotranspiration estimation under limited data in Eastern Africa. Journal of Irrigation and Drainage Engineering 143, 06016015, https://doi.org/10.1061/(ASCE)IR.1943-4774.0001154 (2017).Article 

    Google Scholar 
    Čadro, S., Uzunović, M., Žurovec, J. & Žurovec, O. Validation and calibration of various reference evapotranspiration alternative methods under the climate conditions of Bosnia and Herzegovina. International Soil and Water Conservation Research 5, 309–324, https://doi.org/10.1016/j.iswcr.2017.07.002 (2017).Article 

    Google Scholar 
    Vicente-Serrano, S. M. et al. Recent changes in monthly surface air temperature over Peru, 1964–2014. International Journal of Climatology 38, 283–306, https://doi.org/10.1002/joc.5176 (2018).ADS 
    Article 

    Google Scholar 
    Huerta, A., Aybar, C. & Lavado-Casimiro, W. PISCO temperatura versión 1.1 (PISCOt v1. 1). Lima, Peru: National Meteorology and Hydrology Service of Peru (SENAMHI) https://iridl.ldeo.columbia.edu/SOURCES/.SENAMHI/.HSR/.PISCO/.Temp/ (2018).Lavado Casimiro, W. S., Labat, D., Guyot, J. L. & Ardoin-Bardin, S. Assessment of climate change impacts on the hydrology of the Peruvian Amazon–Andes basin. Hydrological Processes 25, 3721–3734, https://doi.org/10.1002/hyp.8097 (2011).ADS 
    Article 

    Google Scholar 
    Rau, P. et al. Assessing multidecadal runoff (1970–2010) using regional hydrological modelling under data and water scarcity conditions in Peruvian Pacific catchments. Hydrological Processes 33, 20–35, https://doi.org/10.1002/hyp.13318 (2019).ADS 
    Article 

    Google Scholar 
    Olsson, T. et al. Downscaling climate projections for the Peruvian coastal Chancay-Huaral basin to support river discharge modeling with WEAP. Journal of Hydrology: Regional Studies 13, 26–42, https://doi.org/10.1016/j.ejrh.2017.05.011 (2017).Article 

    Google Scholar 
    Lavado-Casimiro, W., Lhomme, J. P., Labat, D. & Loup, J. Estimating reference evapotranspiration (FAO 56 Penman Monteith) with limited climatic data in the Peruvian Amazon-Andes basin. Revista Peruana Geo-Atmosferica 4, 31–43 (2015).
    Google Scholar 
    Hargreaves, G. H. & Samani, Z. A. Reference crop evapotranspiration from temperature. Applied engineering in agriculture 1, 96–99, https://doi.org/10.13031/2013.26773 (1985).Article 

    Google Scholar 
    Laqui, W. et al. Can artificial neural networks estimate potential evapotranspiration in Peruvian highlands? Modeling Earth Systems and Environment 5, 1911–1924, https://doi.org/10.1007/s40808-019-00647-2 (2019).Article 

    Google Scholar 
    Baigorria, G. A., Villegas, E. B., Trebejo, I., Carlos, J. F. & Quiroz, R. Atmospheric transmissivity: distribution and empirical estimation around the central Andes. International Journal of Climatology 24, 1121–1136, https://doi.org/10.1002/joc.1060 (2004).ADS 
    Article 

    Google Scholar 
    Huerta, A. PISCO potential evapotranspiration, https://iridl.ldeo.columbia.edu/SOURCES/.SENAMHI/.HSR/.PISCO/.PET/.Oudin, L., Michel, C. & Anctil, F. Which potential evapotranspiration input for a lumped rainfall-runoff model?: Part 1—can rainfall-runoff models effectively handle detailed potential evapotranspiration inputs? Journal of Hydrology 303, 275–289, https://doi.org/10.1016/j.jhydrol.2004.08.025 (2005).ADS 
    Article 

    Google Scholar 
    Xiang, K., Li, Y., Horton, R. & Feng, H. Similarity and difference of potential evapotranspiration and reference crop evapotranspiration – a review. Agricultural Water Management 232, 106043, https://doi.org/10.1016/j.agwat.2020.106043 (2020).Article 

    Google Scholar 
    Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific data 7, 1–18, https://doi.org/10.1038/s41597-020-0453-3 (2020).Article 

    Google Scholar 
    Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific data 5, 1–12, https://doi.org/10.1038/sdata.2017.191 (2018).Article 

    Google Scholar 
    Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society 77, 437–472, 10.1175/1520-0477(1996)077  2.0.CO;2 (1996).Hersbach, H. et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 1999–2049, https://doi.org/10.1002/qj.3803 (2020).ADS 
    Article 

    Google Scholar 
    Muñoz Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth System Science Data 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021 (2021).ADS 
    Article 

    Google Scholar 
    Aybar, C. et al. Construction of a high-resolution gridded rainfall dataset for Peru from 1981 to the present day. Hydrological Sciences Journal 65, 770–785, https://doi.org/10.1080/02626667.2019.1649411 (2020).Article 

    Google Scholar 
    Llauca, H., Lavado-Casimiro, W., Montesinos, C., Santini, W. & Rau, P. PISCO_HyM_GR2M: A model of monthly water balance in Peru (1981–2020). Water 13, 1048, https://doi.org/10.3390/w13081048 (2021).Article 

    Google Scholar 
    Gubler, S. et al. The influence of station density on climate data homogenization. International Journal of Climatology 37, 4670–4683, https://doi.org/10.1002/joc.5114 (2017).ADS 
    Article 

    Google Scholar 
    Hunziker, S. et al. Identifying, attributing, and overcoming common data quality issues of manned station observations. International Journal of Climatology 37, 4131–4145, https://doi.org/10.1002/joc.5037 (2017).ADS 
    Article 

    Google Scholar 
    Hunziker, S. et al. Effects of undetected data quality issues on climatological analyses. Climate of the Past 14, 1–20, https://doi.org/10.5194/cp-14-1-2018 (2018).ADS 
    Article 

    Google Scholar 
    Paredes, P., Pereira, L., Almorox, J. & Darouich, H. Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables. Agricultural Water Management 240, 106210, https://doi.org/10.1016/j.agwat.2020.106210 (2020).Article 

    Google Scholar 
    Irmak, S., Kabenge, I., Skaggs, K. E. & Mutiibwa, D. Trend and magnitude of changes in climate variables and reference evapotranspiration over 116-yr period in the Platte River Basin, central Nebraska–USA. Journal of Hydrology 420, 228–244, https://doi.org/10.1016/j.jhydrol.2011.12.006 (2012).ADS 
    Article 

    Google Scholar 
    Tomas-Burguera, M., Vicente-Serrano, S. M., Grimalt, M. & Beguera, S. Accuracy of reference evapotranspiration (ETo) estimates under data scarcity scenarios in the Iberian Peninsula. Agricultural water management 182, 103–116, https://doi.org/10.1016/j.agwat.2016.12.013 (2017).Article 

    Google Scholar 
    Mardikis, M., Kalivas, D. & Kollias, V. Comparison of interpolation methods for the prediction of reference evapotranspiration—an application in Greece. Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) 19, 251–278, https://doi.org/10.1007/s11269-005-3179-2 (2005).Article 

    Google Scholar 
    McVicar, T. R. et al. Spatially distributing monthly reference evapotranspiration and pan evaporation considering topographic influences. Journal of Hydrology 338, 196–220, https://doi.org/10.1016/j.jhydrol.2007.02.018 (2007).ADS 
    Article 

    Google Scholar 
    Robinson, E. L., Blyth, E. M., Clark, D. B., Finch, J. & Rudd, A. C. Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data. Hydrology and Earth System Sciences 21, 1189–1224, https://doi.org/10.5194/hess-21-1189-2017 (2017).ADS 
    Article 

    Google Scholar 
    Mohammadi, B. & Moazenzadeh, R. Performance analysis of daily global solar radiation models in Peru by regression analysis. Atmosphere 12, https://doi.org/10.3390/atmos12030389 (2021).Vicente-Serrano, S. M., Beguería, S., López-Moreno, J. I., García-Vera, M. A. & Stepanek, P. A complete daily precipitation database for northeast Spain: reconstruction, quality control, and homogeneity. International Journal of Climatology 30, 1146–1163, https://doi.org/10.1002/joc.1850 (2010).ADS 
    Article 

    Google Scholar 
    Lanzante, J. R. Resistant, robust and non-parametric techniques for the analysis of climate data: Theory and examples, including applications to historical radiosonde station data. International Journal of Climatology: A Journal of the Royal Meteorological Society 16, 1197–1226, 10.1002/(SICI)1097-0088(199611)16:11 < 1197::AID-JOC89 > 3.0.CO;2-L (1996).ADS 
    Article 

    Google Scholar 
    Wood, W. H., Marshall, S. J., Whitehead, T. L. & Fargey, S. E. Daily temperature records from a mesonet in the foothills of the Canadian Rocky Mountains, 2005–2010. Earth System Science Data 10, 595–607, https://doi.org/10.5194/essd-10-595-2018 (2018).ADS 
    Article 

    Google Scholar 
    Guentchev, G., Barsugli, J. J. & Eischeid, J. Homogeneity of gridded precipitation datasets for the Colorado River basin. Journal of Applied Meteorology and Climatology 49, 2404–2415, https://doi.org/10.1175/2010JAMC2484.1 (2010).ADS 
    Article 

    Google Scholar 
    Oyler, J. W., Ballantyne, A., Jencso, K., Sweet, M. & Running, S. W. Creating a topoclimatic daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature. International Journal of Climatology 35, 2258–2279, https://doi.org/10.1002/joc.4127 (2015).ADS 
    Article 

    Google Scholar 
    McAfee, S. A., McCabe, G. J., Gray, S. T. & Pederson, G. T. Changing station coverage impacts temperature trends in the Upper Colorado River basin. International Journal of Climatology 39, 1517–1538, https://doi.org/10.1002/joc.5898 (2019).ADS 
    Article 

    Google Scholar 
    Beguería, S., Vicente-Serrano, S. M., Tomás-Burguera, M. & Maneta, M. Bias in the variance of gridded data sets leads to misleading conclusions about changes in climate variability. International Journal of Climatology 36, 3413–3422, https://doi.org/10.1002/joc.4561 (2016).ADS 
    Article 

    Google Scholar 
    Thevakaran, A. & Sonnadara, D. Estimating missing daily temperature extremes in Jaffna, Sri Lanka. Theoretical and applied climatology 132, 145–152, https://doi.org/10.1007/s00704-017-2082-0 (2018).ADS 
    Article 

    Google Scholar 
    Hubbard, K. Spatial variability of daily weather variables in the high plains of the USA. Agricultural and Forest Meteorology 68, 29–41, https://doi.org/10.1016/0168-1923(94)90067-1 (1994).ADS 
    Article 

    Google Scholar 
    Camargo, M. B. & Hubbard, K. G. Spatial and temporal variability of daily weather variables in sub-humid and semi-arid areas of the United States high plains. Agricultural and forest meteorology 93, 141–148, https://doi.org/10.1016/S0168-1923(98)00122-1 (1999).ADS 
    Article 

    Google Scholar 
    Brugnara, Y., Good, E., Squintu, A. A., van der Schrier, G. & Brönnimann, S. The EUSTACE global land station daily air temperature dataset. Geoscience Data Journal 6, 189–204, https://doi.org/10.5285/7925ded722d743fa8259a93acc7073f2 (2019).ADS 
    Article 

    Google Scholar 
    Gonzalez-Hidalgo, J. C., Peña-Angulo, D., Brunetti, M. & Cortesi, N. MOTEDAS: a new monthly temperature database for mainland Spain and the trend in temperature (1951–2010). International Journal of Climatology 35, 4444–4463, https://doi.org/10.1002/joc.4298 (2015).ADS 
    Article 

    Google Scholar 
    Gudmundsson, L., Bremnes, J. B., Haugen, J. E. & Engen-Skaugen, T. Technical note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods. Hydrology and Earth System Sciences 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012 (2012).ADS 
    Article 

    Google Scholar 
    Stanley, T., Kirschbaum, D. B., Huffman, G. J. & Adler, R. F. Approximating long-term statistics early in the global precipitation measurement era. Earth Interactions 21, 1–10, https://doi.org/10.1175/EI-D-16-0025.1 (2017).Article 

    Google Scholar 
    Haimberger, L. Homogenization of radiosonde temperature time series using innovation statistics. Journal of Climate 20, 1377–1403, https://doi.org/10.1175/JCLI4050.1 (2007).ADS 
    Article 

    Google Scholar 
    Menne, M. J. & Williams, C. N. Homogenization of temperature series via pairwise comparisons. Journal of Climate 22, 1700–1717, https://doi.org/10.1175/2008JCLI2263.1 (2009).ADS 
    Article 

    Google Scholar 
    Vincent, L. A., Zhang, X., Bonsal, B. R. & Hogg, W. D. Homogenization of daily temperatures over Canada. Journal of Climate 15, 1322–1334, 10.1175/1520-0442(2002)015 < 1322:HODTOC > 2.0.CO;2 (2002).ADS 
    Article 

    Google Scholar 
    Jin, M. & Dickinson, R. E. Land surface skin temperature climatology: Benefitting from the strengths of satellite observations. Environmental Research Letters 5, 044004, https://doi.org/10.1088/1748-9326/5/4/044004 (2010).ADS 
    Article 

    Google Scholar 
    Wan, Z., Hook, S. & Hulley, G. MOD11A2 MODIS/Terra land surface temperature/emissivity 8-day l3 global 1 km SIN grid v006. NASA EOSDIS Land Processes DAAC 10, https://doi.org/10.5067/MODIS/MOD11A2.006 (2015).Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS biology 14, e1002415, https://doi.org/10.1371/journal.pbio.1002415 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Danielson, J. J. & Gesch, D. B. Global multi-resolution terrain elevation data 2010 (GMTED2010) (US Department of the Interior, US Geological Survey Washington, DC, USA, 2011).Holden, Z. A., Abatzoglou, J. T., Luce, C. H. & Baggett, L. S. Empirical downscaling of daily minimum air temperature at very fine resolutions in complex terrain. Agricultural and Forest Meteorology 151, 1066–1073, https://doi.org/10.1016/j.agrformet.2011.03.011 (2011).ADS 
    Article 

    Google Scholar 
    Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International journal of climatology 37, 4302–4315, https://doi.org/10.1002/joc.5086 (2017).ADS 
    Article 

    Google Scholar 
    Willmott, C. J. & Robeson, S. M. Climatologically aided interpolation (CAI) of terrestrial air temperature. International Journal of Climatology 15, 221–229, https://doi.org/10.1002/joc.3370150207 (1995).ADS 
    Article 

    Google Scholar 
    Hunter, R. D. & Meentemeyer, R. K. Climatologically aided mapping of daily precipitation and temperature. Journal of Applied Meteorology 44, 1501–1510, https://doi.org/10.1175/JAM2295.1 (2005).ADS 
    Article 

    Google Scholar 
    Parmentier, B. et al. Using multi-timescale methods and satellite-derived land surface temperature for the interpolation of daily maximum air temperature in Oregon. International Journal of Climatology 35, 3862–3878, https://doi.org/10.1002/joc.4251 (2015).ADS 
    Article 

    Google Scholar 
    Hengl, T., Heuvelink, G. B. & Rossiter, D. G. About regression-kriging: From equations to case studies. Computers & Geosciences 33, 1301–1315, https://doi.org/10.1016/j.cageo.2007.05.001. Spatial Analysis (2007).Sun, X.-L., Yang, Q., Wang, H.-L. & Wu, Y.-J. Can regression determination, nugget-to-sill ratio and sampling spacing determine relative performance of regression kriging over ordinary kriging? CATENA 181, 104092, https://doi.org/10.1016/j.catena.2019.104092 (2019).Article 

    Google Scholar 
    Trajkovic, S. & Gocic, M. Evaluation of three wind speed approaches in temperature-based ET 0 equations: a case study in Serbia. Arabian Journal of Geosciences 14, 1–8, https://doi.org/10.1007/s12517-020-06331-5 (2021).Article 

    Google Scholar 
    Fotheringham, A. S., Brunsdon, C. & Charlton, M. Geographically weighted regression: the analysis of spatially varying relationships (John Wiley & Sons, 2003).Comber, A. et al. A route map for successful applications of geographically weighted regression. Geographical Analysis https://doi.org/10.1111/gean.12316 (2021).Li, X., Zhou, Y., Asrar, G. R. & Zhu, Z. Developing a 1 km resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States. Remote Sensing of Environment 215, 74–84, https://doi.org/10.1016/j.rse.2018.05.034 (2018).ADS 
    Article 

    Google Scholar 
    Wu, J., Zhong, B., Tian, S., Yang, A. & Wu, J. Downscaling of urban land surface temperature based on multi-factor geographically weighted regression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, 2897–2911, https://doi.org/10.1109/JSTARS.2019.2919936 (2019).ADS 
    Article 

    Google Scholar 
    Zhang, G., Zhu, S., Zhang, N., Zhang, G. & Xu, Y. Downscaling hourly air temperature of WRF simulations over complex topography: A case study of Chongli District in Hebei Province, China. Journal of Geophysical Research: Atmospheres 127, e2021JD035542, https://doi.org/10.1029/2021JD035542 (2022).ADS 
    Article 

    Google Scholar 
    Callañaupa Gutierrez, S. et al. Seasonal variability of daily evapotranspiration and energy fluxes in the Central Andes of Peru using eddy covariance techniques and empirical methods. Atmospheric Research 261, 105760, https://doi.org/10.1016/j.atmosres.2021.105760 (2021).Article 

    Google Scholar 
    Zotarelli, L., Dukes, M. D., Romero, C. C., Migliaccio, K. W. & Morgan, K. T. Step by step calculation of the Penman-Monteith Evapotranspiration (FAO-56 Method). Institute of Food and Agricultural Sciences. University of Florida https://edis.ifas.ufl.edu/pdf/AE/AE45900.pdf (2010).Huerta, A. PISCOeo_pm, a reference evapotranspiration gridded database based on FAO Penman-Monteith in Peru. figshare https://doi.org/10.6084/m9.figshare.c.5633182.v3 (2021).Willmott, C. J., Robeson, S. M. & Matsuura, K. A refined index of model performance. International Journal of climatology 32, 2088–2094, https://doi.org/10.1002/joc.2419 (2012).ADS 
    Article 

    Google Scholar 
    Legates, D. R. & McCabe, G. J. A refined index of model performance: a rejoinder. International Journal of Climatology 33, 1053–1056, https://doi.org/10.1002/joc.3487 (2013).ADS 
    Article 

    Google Scholar 
    Durre, I., Menne, M. J., Gleason, B. E., Houston, T. G. & Vose, R. S. Comprehensive automated quality assurance of daily surface observations. Journal of Applied Meteorology and Climatology 49, 1615–1633, https://doi.org/10.1175/2010JAMC2375.1 (2010).ADS 
    Article 

    Google Scholar 
    Huerta, A. & Lavado-Casimiro, W. Atlas de Zonas Áridas del Perú: una evaluación presente y futura (Servicio Nacional de Meteorología e Hidrología del Perú, Lima, Perú, 2021).Singer, M. B. et al. Hourly potential evapotranspiration at 0.1° resolution for the global land surface from 1981-present. Scientific Data 8, 1–13, https://doi.org/10.1038/s41597-021-01003-9 (2021).Article 

    Google Scholar 
    Pelosi, A. & Chirico, G. Regional assessment of daily reference evapotranspiration: Can ground observations be replaced by blending ERA5-Land meteorological reanalysis and CM-SAF satellite-based radiation data? Agricultural Water Management 258, 107169, https://doi.org/10.1016/j.agwat.2021.107169 (2021).Article 

    Google Scholar 
    McCuen, R. H. A sensitivity and error analysis cf procedures used for estimating evaporation. JAWRA Journal of the American Water Resources Association 10, 486–497, https://doi.org/10.1111/j.1752-1688.1974.tb00590.x (1974).ADS 
    Article 

    Google Scholar 
    Coleman, G. & DeCoursey, D. G. Sensitivity and model variance analysis applied to some evaporation and evapotranspiration models. Water Resources Research 12, 873–879, https://doi.org/10.1029/WR012i005p00873 (1976).ADS 
    Article 

    Google Scholar 
    Beven, K. A sensitivity analysis of the Penman-Monteith actual evapotranspiration estimates. Journal of Hydrology 44, 169–190, https://doi.org/10.1016/0022-1694(79)90130-6 (1979).ADS 
    Article 

    Google Scholar 
    Hupet, F. & Vanclooster, M. Effect of the sampling frequency of meteorological variables on the estimation of the reference evapotranspiration. Journal of Hydrology 243, 192–204, https://doi.org/10.1016/S0022-1694(00)00413-3 (2001).ADS 
    Article 

    Google Scholar 
    Ali, M. et al. Sensitivity of Penman–Monteith estimates of reference evapotranspiration to errors in input climatic data. Journal of Agrometeorology 11, 1–8, https://journal.agrimetassociation.org/index.php/jam/article/view/1214 (2009).
    Google Scholar 
    Field, C. B., Jackson, R. B. & Mooney, H. A. Stomatal responses to increased CO2: implications from the plant to the global scale. Plant, Cell & Environment 18, 1214–1225, https://doi.org/10.1111/j.1365-3040.1995.tb00630.x (1995).Article 

    Google Scholar 
    Roderick, M. L., Greve, P. & Farquhar, G. D. On the assessment of aridity with changes in atmospheric CO2. Water Resources Research 51, 5450–5463, https://doi.org/10.1002/2015WR017031 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    Swann, A. L., Hoffman, F. M., Koven, C. D. & Randerson, J. T. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proceedings of the National Academy of Sciences 113, 10019–10024, https://doi.org/10.1073/pnas.1604581113 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Milly, P. C. & Dunne, K. A. Potential evapotranspiration and continental drying. Nature Climate Change 6, 946–949, https://doi.org/10.1038/nclimate3046 (2016).ADS 
    Article 

    Google Scholar 
    Greve, P., Roderick, M. L. & Seneviratne, S. I. Simulated changes in aridity from the last glacial maximum to 4xCO2. Environmental Research Letters 12, 114021, https://doi.org/10.1088/1748-9326/aa89a3 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    Scheff, J. Drought indices, drought impacts, CO2, and warming: a historical and geologic perspective. Current Climate Change Reports 4, 202–209, https://doi.org/10.1007/s40641-018-0094-1 (2018).Article 

    Google Scholar 
    Swann, A. L. Plants and drought in a changing climate. Current Climate Change Reports 4, 192–201, https://doi.org/10.1007/s40641-018-0097-y (2018).Article 

    Google Scholar 
    Lemordant, L., Gentine, P., Swann, A. S., Cook, B. I. & Scheff, J. Critical impact of vegetation physiology on the continental hydrologic cycle in response to increasing CO2. Proceedings of the National Academy of Sciences 115, 4093–4098, https://doi.org/10.1073/pnas.1720712115 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R. & Donohue, R. J. Hydrologic implications of vegetation response to elevated CO2 in climate projections. Nature Climate Change 9, 44–48, https://doi.org/10.1038/s41558-018-0361-0 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Greve, P., Roderick, M., Ukkola, A. & Wada, Y. The aridity index under global warming. Environmental Research Letters 14, 124006, https://doi.org/10.1088/1748-9326/ab5046 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Huerta, A. & Gutierrez, L. PISCOeo_pm. figshare https://doi.org/10.6084/m9.figshare.19686738.v1 (2022). More

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    Reply to: Evidence confirms an anthropic origin of Amazonian Dark Earths

    Lombardo et al. argue that, if our hypothesis is correct, ADEs should be continuous rather than patchy. However, alluvium deposition can be a patchy process and the distribution of large and small ADE patches can be predicted regionally based on fluvial geomorphology. For example, 89% of all known ADEs have been predictively mapped using elevation, distance to bluff, and geological provenance as the key predictors (with a false negative rate of 6.5% and a false positive rate of 4.7%)10. Predicted areas include small and large ADE patches, up to several square kilometres in size, and indicate that ADEs cover ~154,000 km2 mostly in central and western Amazonia. This may seem to be a very large area ( >3% of the Amazon basin) but it is only a fraction of the projections found in some of the most cited anthropogenic theory literature11. Assuming the same excess fertility observed at our site, the creation of those ADEs would have required a prohibitive amount of biomass burning, in areas 800–1680 times larger (Fig. 1), which is inconsistent with the centralised small-scale deposition proposed by Lombardo et al. In this regional scenario, it remains unclear how many Amazons would have been needed to build the already-mapped ADEs.Lombardo et al. centre their opinion on settlements in other parts of the Amazon basin, under different socioecological and geomorphological contexts, and where the data we have developed are not available for comparison. Their narrative conflates the Brazilian lowland with other regions, such as the Llanos de Moxos and other systems in the Bolivian-Peruvian foreland basins, where older archeological sites occur. Their comments about the mineral composition of ADEs appear to contradict recent discoveries (made by some of their co-authors)12 which show that some oxides found at our ADE site bear “no relationship to anthropogenic activity” because “their sources are attributed to the weathering of micas, feldspars, mafic minerals (pyroxene), and sodic plagioclase” that are not found locally. To explain the inconsistency between those findings and the current theory of ADE formation, Macedo et al. argue that “sediment depositions in floodplain soils” that “are not related to human occupation” should be considered. That suggestion is consistent with our data which indicate deposition of exogenous materials to the site prior to the invention of agriculture in central Amazonia.Our study area is on a Tertiary terrace, and we acknowledge in our paper that it lies above the modern 100-year flood height for Manaus. However, significant Pleistocene and Holocene tectonic activity and river aggradation/degradation demonstrably affected the flood height over time. A complex neotectonic history has affected terrace elevations, nutrient deposition, and remobilisation, as well as flood heights and aggradation, resulting in higher base levels that were many metres above flood waters today in past millennia13,14,15. In addition, rivers transported and dispersed sediments from the Andes to the lowland, which were re-mobilised, and re-deposited in patchy patterns, from floodplains several times between 20 and 5 thousand years ago16,17,18. Such mineral inputs by past avulsion events may have occurred earlier in the Quaternary and remain as a relict soil where it has not subsequently eroded19. The older weathered sediments on the upper terraces lining the river look nothing like recent alluvium and the distribution of elements and their assemblages at our site are consistent with alluvial deposits in other sites. This process is explained in studies cited by Lombardo et al. (e.g., Pupim et al.), which note several periods of river aggradation, that support our hypothesis.As explained in our original paper, our data do not preclude a more recent human effect on the local landscape. The wisdom of indigenous populations, manifested in the application of waste materials to agricultural sites (since at least the late Holocene), may have further enriched ADEs or countered their natural degradation. Recent studies12, 16, 17, which post-date the studies that Lombardo et al. cite to argue against a geogenic influence, reveal a dynamic neotectonic history and support our hypothesis. Thus, the extent to which other ADE sites originated from depositional processes should be investigated based on evidence that goes beyond those presented by Lombardo et al. More