Ecology

Experimental design and crop managementField experiments were conducted at Chengyang Agricultural Experimental Station, Qingdao, China (36°18′ 11″/N, 120°21′ 13″/E) in 2019 and 2020. The soil type in the field was brown loam that contained 22.76 g kg−1 organic matter, 82.39 mg kg−1 alkali-hydrolysable N, 25.10 mg kg−1 Olsen-P and 94.89 mg kg−1 exchangeable K. The test cultivars of maize were Jinhai5 with strong lodging resistance and Xundan20 with weak lodging resistance, which were repeated four times in plots laying out in randomized block designs. Plant density was 7.5 plants / m2 with the row spacing of 60 cm. the plot consisted of 8 rows length of 15 m. Two–three seeds per hole were manually sowed at 5 cm on 20 April 2019 and 24 April 2020, and the seedlings were thinned to the target planting density at V2, and harvested on 10 September and 14 September, respectively. Fertilization and irrigation management followed local production practices in maize.Sampling and measurementPlant samples were taken at V8, V12, R1, R2 and R6. Ten typical plants of each tested cultivars were selected to be subjected to mechanical and above-ground morphological measurements at each sampling. The other three maize plants were used to measure morphological traits of roots. Xundan20 was seriously damaged due to the storm in the late stage of maize growth in 2020, resulting in the missing data for physiological maturity.Determination of leaf area vertical distributionLeaf area of expanded leaves each was computed by the coefficient method: Single leaf area = length * width * 0.75. Leaf area for unexpanded leaves was estimated by the leaf weight method. Leaf area per plant was the sum of all individual green leaf areas. Leaf height is the height from the ground to the leaf collar position of maize.Determination of max root side-pulling resistanceSample plants were surrounded with water-proof steel devices inserted into underground, and watered to soil moisture over saturation at one day before mechanical testing. When measured, due to the limited space, all leaves of sample plants are removed in order to improve the measurement accuracy. The defoliated stalks were immobilized by a pair of lengthwise steel clamps to prevent stalks from bending (Fig. 7). After the digital pole dynamometer18 with a 1.5 m long slider and a main unit was linked to the stalks at a height of 80 cm away from the ground, the operator by hand pulled at a slow and uniform speed until the roots were pulled out. Records of load force, declination angle and sensor position were automatically stored in main unit during this operation. The peak value of forces, extracted from records, was taken as the max root side-pulling resistance.Figure 7Schematic diagram for measuring max root side-pulling resistance.Full size imageRoot anti-lodging indexBased on the method of Cui et al.6, the force value comparison is changed to the moment value comparison to calculate root anti-lodging index:$${text{AL}}_{root} = M_{root} / , M_{wind} = F_{root} / , F_{wind}$$
(1)
where M root is the root failure moment, M wind is the wind resultant moment. Root anti-lodging index indicates the ability of plants to resist root lodging. The larger its value is, the stronger the resistance is, and vice versa.$${text{M}}_{root} = F , *d$$
(2)
where F is the max root side-pulling resistance, d is moment arm, i.e., the length of force arm. As a component of root anti-lodging index, the root failure moment represents the ability of the root system to resist lateral pulling. The greater its value is, the better the resistance is, and vice versa.With the base of the stem as the fulcrum,$${text{M}}_{wind} = sum 0.{5}CA_{i} rho V^{2} h_{i}$$
(3)
where C is coefficient of air resistance, ρ is air mass density ,V is the wind speed , Ai is the area of a single leaf , hi is the height of leaf, ∑ represents to sum up over all leaves. C value is set to be 0.219. When encountering wind speed at grade 6 or higher, maize is more prone to lodging. Unless stated explicitly, the following analysis was limited to the upper wind speed for grade 6 wind20.Root morphological traitsThe number and length of all primary nodal roots were measured. Root-soil balls each of two or three tested plants were obtained after lateral root-pulling testing. The images of the three frontal sides, 120 degrees apart from each other, of the root-soil balls were taken using a digital camera. Ball volumes were then evaluated by considering them to be rotationally symmetric. Average volumes were used for further analysis.Single root tensile resistanceRoots after counting the number of nodal roots were used to measure the single root tensile resistance. First, clean the dust off roots. Then, diameters of roots were determined with a vernier caliper. Single root tensile resistance was measured by HF-500 digital push–pull apparatus. Fixed the upper and lower ends of the root, then one end moved slowly and uniformly, the other end was still until the root breaks. The peak tension force displayed by the instrument was taken as the single root tensile resistance.Statistical analysisBased on variance analysis, the Tukey method was used to compare the differences among means. The logarithmic transformation of variables was carried out to improve the homogeneity of error variance if appropriate.The substantive effect or influence of various factors on the response variable can be expressed by effect size of factors, which can be calculated under the framework of variance analysis. Effect size is the proportion of the effect of a certain factor in the total effect, which is a dimensionless number21,22,23.The formula for calculating effect size of factors is:$$omega^{2} = frac{{df_{effect} times left( {MS_{effect} – MS_{error} } right)}}{{SS_{total} + MS_{error} }}$$
(4)
where df is the degree of freedom, MS represents mean square.Two conceptual models were used when dealing with effect size. One model was of components, i.e., taking the logarithm of both sides of Eq. (1):$${text{LOG}}left( {{text{AL}}_{{{text{root}}}} } right) , = {text{ LOG}}left( {{text{M}}_{{{text{root}}}} } right) , + {text{ LOG}}left( {{text{M}}_{{{text{wind}}}} } right)$$
(5)
where LOG denotes logarithmic transformation.The other was the factorial model, i.e.,$${text{factors affecting AL}}_{{{text{root}}}} = {text{ wind grade }} + {text{ cultivar }} + {text{ growth stage}}$$
(6)
Experimental research and field studies on plants including the collection of plant materialThe authors declare that the cultivation of plants and carrying out study in Chengyang Agricultural Experimental Station complies with all relevant institutional, national and international guidelines and treaties.Statement of permissions and/or licenses for collection of plant or seed specimensThe authors declare that the seed specimens used in this study are publicly accessible seed materials and we were given explicit written permission to use them for this research. More

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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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Here we have used existing surveillance to detect an emerging wildlife disease and appraise its impact by combining traditional host and vector screening with utilisation of national datasets generated by citizen scientists. Following the detection of USUV in the UK in 20207, whilst national surveillance identified no further cases of USUV infection in wild birds that year, we discovered a significant cluster of blackbird DIRs and an overlapping regional reduction in reported blackbird observations, possibly indicating disease-mediated population decline. Our investigation also identified mosquito vectors at the index site that were positive for USUV RNA, suggesting that ongoing virus transmission was likely.The most prevalent and notable histological changes in the blackbirds and house sparrow with confirmed USUV infection were those in the liver and spleen, consisting of necrosis and lymphohistiocytic inflammation along with moderate to abundant virus antigen labelling. Whilst neurotropism resulting in brain necrosis and lymphohistiocytic inflammation has been reported in studies which examined large numbers of wild blackbirds with USUV infection in continental Europe4,12, we found minimal evidence of neural lesions in the five wild birds examined in this study. Although, histopathological changes in other tissues were generally non-specific, immunolabelling demonstrated widespread virus antigen distribution in both bird species, which is similar to reports of USUV infection elsewhere4,13,14. Immunolabelling was disproportionately greater in the brain and heart in contrast to the minimal or absent histological changes observed in these organs: similar contrasting results of histological and immunohistochemical examinations of USUV-infected wild birds have previously been reported12. Although only brain and kidney samples were examined using USUV RT-PCR, our findings, together with earlier reports4,14, demonstrate that viral antigen can be detected in abundance in the heart and liver, suggesting that these organs could be useful for molecular diagnostic sampling.A differential for necrotising lesions in European passerines, and a comorbidity detected in blackbirds with USUV infection, is Plasmodium spp. infection4,8,15. DNA of the same Plasmodium spp. as detected in the tissues of USUV-positive blackbirds from the ZSL London Zoo site in 2020 was identified in Cx. pipiens s.l. that fed on blackbird hosts at this site previously in 2015, supporting endemic avian haemoparasite infection of this wild bird species at this location. In contrast to the results reported from USUV-positive blackbirds in the Netherlands4, no exo-erythrocytic stages of haemoprotozoa indicative of avian malaria were observed histologically in the two UK blackbirds positive for Plasmodium DNA. Since histological examination has limited sensitivity, in situ hybridisation could be used to further appraise the clinical significance of this co-infection in the future16.Zoological collections are ideally placed to form part of wildlife disease surveillance networks and have already contributed to flavivirus detection in mainland Europe10,13,17,18. The collection grounds at ZSL London Zoo are well monitored for evidence of morbidity or mortality in synanthropic wildlife; this unusually high level of vigilance is considered the likely explanation for detection of USUV at such a location. Recent import of infected captive birds can be excluded as a potential route of USUV introduction as the COVID-19 pandemic had led to suspension of animal movements into the zoological collection. Following USUV detection in synanthropic wildlife, preemptive management practices were employed to safeguard the health of captive animals (Supplementary Materials 1); there was no evidence of USUV-associated disease in the collection animals.The majority of mosquitoes trapped in 2020 were primarily ornithophagic Cx. pipiens s.l., a known vector for USUV1 and a common species in temperate urban habitats. This mosquito species was also the most frequently detected at the ZSL London Zoo site in 2015, during historical trapping sessions19 and at two zoological collections in northern England20. Bloodmeal analyses from mosquitoes at ZSL London Zoo in 2015 and 2020 demonstrate that this species feeds on both wild and collection birds, as would be expected for a generalist ornithophagic mosquito21. In addition, targeted mosquito surveillance in 2020 confirmed circulating USUV in multiple Cx. pipiens s.l. pools at the index site over a three-week period subsequent to the detection of USUV-associated wild bird mortality. This further demonstrates that local mosquito trapping combined with PCR screening is useful as part of an integrated surveillance programme22 and provides evidence that native vectors in the UK may facilitate the onward transmission of USUV to susceptible hosts following an emergence event.Wild bird flavivirus surveillance in Great Britain integrates submissions from three schemes, each with a different taxonomic focus. These convenience samples inevitably lead to skews in species coverage. Although a common garden bird, the number of blackbirds tested for USUV was modest at 2–8 per annum over the period 2012–2019. A communication programme to raise awareness of blackbirds as a sentinel species for USUV, involving a range of stakeholder communities (e.g. non-governmental organisations, wildlife rehabilitators and veterinary surgeons) could help to increase the volume of submissions and, by extension, the ability to rapidly identify the occurrence of USUV in this species. The potential value of target species as sentinels within wild bird surveillance networks has been highlighted for other pathogens, e.g. highly pathogenic H5N1 avian influenza and West Nile virus23,24. In addition to this passive surveillance focused on disease detection in avian hosts, active targeted serosurveys could be conducted to identify cryptic exposure of subclinically affected birds in the future. Given the logistical challenges around active serosurveys in wild birds, screening of archived samples from captive birds in the zoological collection may provide a means to further appraise the extent of USUV circulation, as has previously been undertaken at other collections in mainland Europe25,26.Local reductions of blackbird populations have been reported following USUV outbreaks in mainland Europe27,28,29, but numbers recorded by the BBS have been stable in the UK and Greater London since 2011 when USUV incursion would be predicted most likely to have occurred on the basis of spatio-temporal patterns of spread in mainland Europe3 until the latest data are available from 2019 (Supplementary Figure 5). Whilst our index site detection of USUV is unlikely to represent the incursion event, and earlier sporadic or localised USUV incidents prior to 2020 may have occurred7, based on historical blackbird population trends it seems plausible that the existing surveillance system enabled rapid detection of this emerging infectious disease.Significant clustering of blackbird DIRs was observed in the Greater London, South East and East of England regions in 2020. These results should be interpreted with care given the potential for biases with these opportunistic data and the absence of confirmed aetiology for the DIRs, however, these findings are consistent with a regional increase in blackbird morbidity and mortality in summer 2020 around the USUV index site. Consequently, it is likely that further blackbirds, in addition to those recovered for PME, were infected and died with USUV. Whilst no evidence of an increase in generalised ill health or neurological disease category blackbird DIRs was found in 2020, particular attention should be paid to early detection of clusters of DIRs of these categories as a potential signal of USUV occurrence in the future.One indicator, the dead bird ringed recovery dataset, did not support increased scale of blackbird mortality in Greater London; however, the dataset is small and vulnerable to variation in observer bias (e.g. related to COVID-19 induced lockdown and travel restrictions). In contrast, using the GBW dataset, we identified a substantial seasonal decline in the blackbird weekly reporting rate which was associated with a concomitant reduction in weekly count in gardens, but not in ecologically similar control species, which was contemporaneous with the period of detected USUV activity in Greater London. These population trends are consistent with a hypothesis of disease-mediated decline. Alternative explanations, such as variation in climate, food availability or bird movement need consideration and are discussed next.Exploration of climate data indicates that, whilst the spring and early summer of 2020 was noteworthy with a high daily temperature average and low rainfall, at the time of USUV detection and the decline in the blackbird reporting rate, these parameters were within historical ranges (Supplementary Table 7). Consequently, while the climate may have been permissive for USUV transmission, there is no evidence to support variation in the weather alone as an explanation for the seasonal pattern of blackbird reporting rate decline; nor were declines observed in the robin or starling data, the control species with similar soil invertebrate diet and therefore similar vulnerability to summer drought. Blackbird, robin and starling populations in the UK are partially migratory; however, birds from mainland Europe do not migrate to overwinter in England until mid-October (i.e. after the decline in blackbird reporting rate occurred): consequently international bird movement does not offer an explanation for the observed regional blackbird decline. During the late summer season, short-distance movement from garden to non-garden habitats typically occurs, during the period of moult; however, the extent of the decline in blackbird reporting rate in gardens that occurred in Greater London in 2020 markedly exceeds that of the historical trend (2011–2019 inclusive; BTO unpubl. data). In summary, despite the fact that surveillance did not confirm further cases of wild bird USUV infection in 2020, and whilst it is not possible to ascribe causality, or exclude the chance that other factors may have contributed to the observed population trend, it remains possible that large-scale blackbird mortality due to USUV occurred in Greater London in summer 2020.Our study and others30 illustrate the need to integrate disease surveillance and long-term population monitoring schemes to evaluate disease impact, and to use control species to explore potential confounding drivers of population change (e.g. climate, food availability). Since GBW reporting rates are generated online in real-time, and nationwide, they offer a tool to rapidly detect changes in species presence (i.e. reporting rate) or flock size in gardens (i.e. weekly maximum count) that can be used to strategically enhance surveillance effort for disease detection. As wild bird ring recovery reports are also submitted online, there is also the potential to develop a complementary system that monitors for trends in occurrence of dead birds that might signal a disease outbreak. The BBS survey provides the most robust available data on population trends to appraise disease impact, however there is a delay of some months until data from this scheme become available. Since repeated incursions have occurred in mainland Europe following first detection1,17,31, it is likely that USUV will emerge in the UK again, either through overwintering or repeat incursion(s). Integrated disease surveillance in combination with bird population monitoring using the various available datasets, as we have capitalised on here, is required to assess whether USUV re-occurs, or becomes endemic, in UK wild birds and to identify any associated population impacts.By combining a range of professional and citizen science datasets our study approach facilitates the rapid detection of an emerging disease in free-living wildlife and enables insights into its incipient impact. We believe this multidisciplinary approach presents a framework for the early detection of disease outbreaks and incursion, thus helping to safeguard animal and public health. Such early warning systems could facilitate prompt mitigation action, for example targeted biosecurity measures and enhanced vigilance by medical and veterinary authorities. In addition, there is opportunity to further develop collaboration with ornithologists through active surveillance of wild birds, as was recently employed to detect West Nile virus in a migratory bird in the Netherlands32. Whilst population monitoring schemes are most developed for wild birds, lessons learned may be applied for the surveillance of diseases affecting other taxa. More

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