Philippa Kaur

1500-year stable carbon and oxygen isotopes in larch tree-ring celluloseThe δ13Ccell (Fig. 1a, Fig. S2) and δ18Ocell (Fig. 1b, Fig. S3) records span 516–2016 CE, at annual resolution. The δ13Ccell timeseries shows mostly increasing trends during the first millennium of the Common Era (516–1120 CE), and similarly at the end of the last millennium (1720–2016 CE). The maximum δ13Ccell value occurs in 2016 CE (−19.6‰; + 3.2σ), while the minimum occurs in 686 CE (−24.7‰, −3.6σ) relative to the average for the period 516–2016 CE (−22.04‰) (Table S2, Fig. S2). The standard error (SE) for the whole analysed period is 0.02.Figure 1Annually resolved δ13Ccell (a) and δ18O cell (b) in Siberian larch tree-ring cellulose chronologies for the period from 516 to 2016 CE. Chronologies are smoothed by a 101-year Hamming window to highlight a centennial scale. The dotted and dashed lines indicate the number of trees analysed.Full size imageThe δ18Ocell timeseries (Fig. 1b, Fig. S3) showed two positive and one negative extreme over the past 1500 years, with the minimum value (19.9‰; −6.3σ), occurring in 536 CE, and maximum values (31.9‰; + 3.8σ and 32.2‰; + 4.4σ), occurring in 1266 and 2008 CE, respectively (Table S2, Fig. S3). The SE for the whole analysed period is 0.03. The δ18Ocell data has higher standard deviation (SD) (1.15) than δ13Ccell (0.75).Less than 1% of values in the δ18Ocell record are classified as extreme, with the standard deviation ≥  ± 3σ. The δ13Ccell and δ18Ocell records are significantly correlated (r = 0.1, p = 0.0001, n = 1500).Local climate signals preserved in δ13Ccell and δ18Ocell recordsWe used weather observations from the local Mugur-Aksy weather station (50°N, 90°E, 1850 m asl) (Table S1) to derive quantitative paleoclimatic reconstructions from our δ13Ccell and δ18Ocell timeseries. A multiple linear regression analysis revealed significant correlations between δ13Ccell and July precipitation (r = −0.58; p  More

Virus propagation and enumerationEchovirus-11 (E11, Gregory strain, ATCC VR737) and Coxsackievirus-A9 (CVA9, environmental strain from sewage, kindly provided by the Finnish National Institute for Health and Welfare) stocks were produced by infecting sub-confluent monolayers of BGMK cells as described previously [7]. Viruses were released from infected cells by freezing and thawing the culture flasks three times. To eliminate cell debris, the suspensions were centrifuged at 3000 × g for 5 min. Each stock solution was stored at −20 °C until use. Infectious virus concentrations were enumerated by a most probable number (MPN) infectivity assay as described in the Supplementary Information. The assay limit of detection (LoD), defined as the concentration corresponding to one positive cytopathic effect in the lowest dilution of the MPN assay under the experimental conditions used, corresponding to 2 MPN/mL.Inactivation of enteroviruses by bacterial consortia from lake waterTo study the inactivation of CVA9 and E11 by a bacterial consortium from lake water, four surface water samples were collected from Lake Geneva (Ecublens, Switzerland) during the summer 2021. Each sampling event was conducted on warm and sunny days, to minimize biological variation. Immediately after sampling, large particles of the sample were removed by filtering 500 mL of water on a 8 μm nitrocellulose filter membrane (Merck Millipore, Cork, Ireland). The sample was then filtered through a 0.8 μm nitrocellulose filter membrane (Merck Millipore) to remove large microorganisms such as protists. The resulting water sample corresponds to the bacterial fraction used to study virus inactivation.For inactivation experiments, each virus was spiked into individual 1 mL aliquots of fractionated lake water to a final concentration of 106 MPN/mL, and samples were incubated for 48 h at 30 °C without shaking. Duplicate experiments were conducted for each virus and each lake water sample. Experiments to control for thermal inactivation were conducted using the same procedure but by replacing the fractionated lake water with sterile milliQ water. Viral infectivity at times 0 h and 48 h was determined by MPN as described above. Virus decay was calculated as log10 (C/C0), where C is the residual titer after 48 h of incubation, and C0 is the initial titer. The experimental LoD was approximately 5-log10.These same experiments were conducted for three new water samples in the presence of four protease inhibitors with the following final concentrations: E64—10 μM (E3132, Sigma–Aldrich, Saint-Louis, MO, USA), GM6001—4 μM (CC1010, Sigma–Aldrich), Chymostatin—100 μM (C7268, Sigma–Aldrich), and PMSF—100 μM (P7626, Sigma–Aldrich). Each inhibitor was added to 1 mL of fractionated lake water, vortexed for 30 seconds, and incubated at room temperature for 15 min, before adding the two viral strains under the same conditions as described above.Bacterial isolation, cultivation, and storageBacteria were isolated from two water samples from Lake Geneva’s Ecublens beach, taken in November 2019 (Fall, 89 isolates) and May 2020 (Spring, 47 isolates). Bacteria recovery was performed on R2A agar plate (BD Difco, Franklin Lakes, NJ, USA) as described previously [15]. Briefly, successive dilutions from 10−1 to 10−5 were carried out in sterile water for each sample. For each dilution, a volume of 1 mL was deposited on three separate R2A plates, before being incubated at 22, 30, and 37 °C. After 5 days of incubation, each colony was picked and enriched on a new R2A plate. To ensure purity, each isolate was successively plated five times on R2A plate and incubated at the same temperature as the initial isolation. Each purified isolate was cryopreserved in R2A / 20% glycerol at −80 °C. The isolates were named based on the water body (Lake (L)), isolation temperature, and the isolation order (L-T°C-number).Bacterial identificationThe identification of each isolate was performed by 16 S rRNA gene sequencing using the pair of primers 27 F (5’- AGA GTT TGA TCM TGG CTC AG- 3’, Microsynth AG, Balgach, Switzerland) / 786 R (5’- CTA CCA GGG TAT CTA ATC – 3’, Microsynth AG), following a methodology previously described [15]. The thermocycling conditions and the purification of PCR products are described in the Supplementary Information. The complete list of isolated bacteria and associated accession numbers is given in Supplementary Table 1.Phylogenetic inference and metadata visualizationThe consensus from 16 S rRNA gene sequences of the 136 isolates was aligned using the MUSCLE algorithm [16]. The phylogenetic analysis of 566 bp aligned sequences from the V2-V4 16 S rRNA gene regions (Positions: 152–717) was performed using Molecular Evolutionary Genetics Analysis X software [17]. Phylogeny was inferred by maximum likelihood, with 1000 bootstrap iterations to test the robustness of the nodes. The resulting tree was uploaded and formatted using iTOL [18].Virus incubation with bacterial isolatesFor the preparation of the bacteria before co-incubation, each one was first cultured on R2A agar for 48 h at their initial isolation temperature. Overnight suspensions of each bacterial isolate were grown in R2A broth at room temperature under constant agitation (180 rpm). For co-incubation experiments, 200 μL of each bacterial suspension were mixed with 100 μL of a 105 MPN/mL stock of E11 or CVA9. Then, each condition was supplemented with 600 μL of R2A broth. Incubation was carried out for 96 h at room temperature, without shaking. At the end of the co-incubation, each tube was centrifuged for 15 min at 9000 × g (4 °C) to eliminate bacteria, and the residual infectious viral titer was enumerated by MPN assay as described above [7]. Each co-incubation experiment was carried out in triplicate. Control experiments were performed under the same conditions but using sterile R2A. Virus decay was quantified as log10 (Cexp/Cctrl), where Cexp is the residual titer after a co-incubation for 96 h, and Cctrl is the titer after incubation of the virus in sterile R2A for 96 h. The experimental LoD was 3-log10.Protease activity measurement using casein and gelatin agar platesCasein agar was prepared as follows: 20 g of skim milk (BD Difco), supplemented with 1 g glucose were reconstituted with 200 mL of distilled water. Likewise, a 10% bacteriological agar solution was prepared in a final volume of 200 mL. Finally, a solution consisting of 0.8% NaCl, 0.02% KCl, 0.144% Na2HPO4, and 0.024% KH2PO4 was reconstituted in 600 mL of water. All solutions were autoclaved for 15 min at 110 °C. The solutions were mixed, and 25 mL were poured into each Petri dish. Gelatin agar was composed of 0.4% peptone, 0.1% yeast extract, 1.5% gelatin and 1.5% bacteriological agar. The mixture was autoclaved 15 min at 120 °C, and 25 mL of medium was poured into each Petri dish.For each isolate, an overnight suspension was performed in R2A broth at room temperature, before spotting 15 μL of each suspension at the center of both gelatin and casein agar plates. Each plate was incubated at 22, 30, or 37 °C for 72 h, depending on the initial isolation temperature of the bacteria. Casein-degrading activity (cas), which is exerted by many different protease classes, and gelatin-degrading activity (gel), which is mostly caused by MMPs, were revealed by a hydrolysis halo around the producing bacteria. Hydrolysis diameters were measured in millimeters (mm) to report the extent of the proteolytic effect of each strain on both substrates.Protease activity quantification in cell-free supernatantUsing the same bacterial suspensions as for bacterial/virus co-incubation, 200 μL of each suspension was inoculated into 600 μL of R2A broth and incubated without shaking for 96 h at room temperature. Each culture was centrifuged for 15 min at 9000 × g at 4 °C. The resulting cell-free supernatants (CFS) were stored at −20 °C until use. For each CFS, protease activity was measured using the Protease Activity Assay Kit (ab112152, Abcam, Cambridge, UK), which measures general protease activity (pgen) except MMPs, and the MMP Activity Assay Kit (ab112146, Abcam), which selectively measures MMP activity (mmp). Briefly, for the Protease Activity Assay kit, 50 μL of the substrate was added into each well of a dark-bottom plate containing 50 μL of each CFS. Standard trypsin provided by the kit was used as a positive control. For the MMP Activity Assay kit, 50 μL of each CFS was incubated with 50 μL of 2 mM APMA for 3 h at 37 °C, prior to the activity test. Collagenase I (C0130, Sigma–Aldrich) was used as a positive control. R2A broth was used as a negative control for each assay. Protease activity was measured at time 0 and after 60 min, using a Synergy MX fluorescence reader (BioTek). The excitation and emission wavelengths were set to 485 and 530 nm, respectively. The emitted fluorescence, generated by proteolytic cleavage of the substrate of each kit, was calculated as follows: ∆RFU = RFU (60 min) − RFU (0 min). Proteolytic activity was calculated in mmol/min/μL based on the emitted fluorescence measured for trypsin and collagenase I at known proteolytic activities.Data analysisStatistical analyses to compare inactivation data were performed by one-way t-test or one-way ANOVA with Dunnett’s post-hoc test in GraphPad Prism v.9. An alpha value of 0.05 was used as a threshold for statistical significance. For each dataset we confirmed that data were normally distributed.To analyze a potential correlation between protease activity and viral decay, the decay values for each virus strain was related to the four protease activity tests of this study using a scatterplot combined with a Kernel density estimation. The analyses were performed with R v.3.6.1 using the SmoothScatter function of the R Base package.A Left-Censored Tobit model (CTM) with mixed effects was chosen to investigate interactions between protease activity and the decay measured for each virus strain. Briefly, the CTM with mixed effect was chosen for three reasons: (1) The protocol used to measure viral decay had a limit of quantification of −3-log10, and 152 measurement points reached the detection limit, requiring the use of this value as the left-censored value of the model; (2) The two virus strains used in the study showed distinct responses after exposure to environmental bacteria, preventing the use of a multiple linear regression model; (3) Among biological replicates of co-incubation experiments, inactivation variability was observed, suggesting the concomitant action of random biological effects (e.g., production of other compounds than proteases by bacteria, or differences in protease production rate between replicates for each bacterial isolate). The resulting statistical model was then formulated as follows:$$log left( {frac{{C_{{{{{{mathrm{exp}}}}}}}}}{{C_{{{{{{mathrm{ctrl}}}}}}}}}} right) = ; beta _0 + beta _1;{rm I}_{{{{{{{{mathrm{virus}}}}}}}}_i = 2} + beta _2sqrt {left[ {pgen} right]_i} + beta _3sqrt {left[ {mmp} right]_i} + beta _4sqrt {left[ {cas} right]_i} \ + beta _5sqrt {left[ {gel} right]_i} + beta _6I_{{{{{{{{mathrm{virus}}}}}}}}_i = 2}sqrt {left[ {pgen} right]_i} + beta _7I_{{{{{{{{mathrm{virus}}}}}}}}_i = 2}sqrt {left[ {mmp} right]_i} \ + beta _8I_{{{{{{{{mathrm{virus}}}}}}}}_i = 2}sqrt {left[ {cas} right]_i} + beta _9I_{{{{{{{{mathrm{virus}}}}}}}}_i = 2}sqrt {left[ {gel} right]_i} + alpha _{{{{{{{{mathrm{id}}}}}}}}_i} + varepsilon _i$$$${{{mbox{where}}}}; log left( {frac{{C_{{{{{{mathrm{exp}}}}}}}}}{{C_{{{{{{mathrm{ctrl}}}}}}}}}} right) = left{ {begin{array}{*{20}{c}} { – 3} & {{{{{{{{mathrm{if}}}}}}}};{{{{{{{mathrm{log}}}}}}}}left( {frac{{C_{{{{{{mathrm{exp}}}}}}}}}{{C_{{{{{{mathrm{ctrl}}}}}}}}}} right) le – 3} \ {{{{{{{{mathrm{log}}}}}}}}left( {frac{{C_{{{{{{mathrm{exp}}}}}}}}}{{C_{{{{{{mathrm{ctrl}}}}}}}}}} right)} & {{{{{{{{mathrm{otherwise}}}}}}}}} end{array}} right.$$$$alpha _{{{{{{{{mathrm{id}}}}}}}}_i}sim {{{{{{{mathrm{i}}}}}}}}.{{{{{{{mathrm{i}}}}}}}}.;{{{{{{{mathrm{d}}}}}}}}.;{rm N}left( {0,;sigma _{{{{{{{{mathrm{id}}}}}}}}}^2} right)$$$${{{{{{{mathrm{for}}}}}}}};i in left{ {1,2, ldots } right}$$for which β0 defines the model intercept, (beta _1{rm I}_{{{{{{{{mathrm{virus}}}}}}}}_i = 2}) corresponds to the main effect of the virus factor on the viral decay, (beta _2,;beta _3,;beta _4,;{{{{{{{mathrm{and}}}}}}}};beta _5) corresponds to the main effects of the different protease activity measurements on viral decay, (beta _6I_{{{{{{{{mathrm{virus}}}}}}}}_i = 2},;beta _7I_{{{{{{{{mathrm{virus}}}}}}}}_i = 2},;beta _8I_{{{{{{{{mathrm{virus}}}}}}}}_i = 2},{{{{{{{mathrm{and}}}}}}}};beta _9I_{{{{{{{{mathrm{virus}}}}}}}}_i = 2}) corresponds to the interaction effects between each of these variables and the viral decay, (alpha _{{{{{{{{mathrm{id}}}}}}}}_i}) corresponds to the mixed effect of the model and (varepsilon _i) corresponds to the error term of the model. The selection of the model is further detailed in the Supplementary Information (Supplementary Material and Figs. S1 and S2).The full dataset included in the correlation analysis and the CTM is provided in Supplementary Table 2. A description of the variables used is given in the Supplementary Information. The dataset was analyzed using the censReg package in R [19]. The R code is given in the Supplementary Information. More

We selected eleven major wheat production provinces of China for the study area, which comprise the largest winter wheat-sowing fraction: Henan, Shandong, Anhui, Jiangsu, Hebei, Hubei, Shanxi, Shaanxi, Sichuan, Xinjiang, and Gansu (Fig. 1). The wheat planting area is about 22 million ha in these provinces, accounting for more than 93% of the total wheat planting area. The total wheat production in these regions contributes more than 96% of the total wheat production in China, with more than 128 million tons in 201933.We developed a methodological framework for high-resolution AGB mapping. It mainly includes three parts: (1) Data acquisition and processing. (2) The WOFOST model parameterization and calibration. (3) Data assimilation (Fig. 1). Each part is explained in more detail below.Data acquisition and processingMeteorological dataChina Meteorological Forcing Dataset34,35 is used as weather driving data for the WOFOST model. The dataset is based on the internationally existing Princeton reanalysis data, Global Land Data Assimilation System data, Global Energy and Water Cycle Experiment-Surface Radiation Budget radiation data, and Tropical Rainfall Measuring Mission precipitation data. It is made by fusing the conventional meteorological observation data of the China Meteorological Administration. It includes seven elements: near-surface air temperature, air pressure, near-surface total humidity, wind speed, ground downward shortwave radiation, ground downward longwave radiation, and ground precipitation rate. The meteorological drive elements required for WOFOST are daily radiation, minimum temperature, maximum temperature, water vapor pressure, average wind speed, and precipitation. Details of these variables that participated in the WOFOST model can be referred to in Table S1.Soil characteristics measurements and phenology observationsSoil and phenology data were collected at 177 agricultural meteorological stations (AMS) from 2007 to 2015 (Fig. 1). Soil characteristics include soil moisture content at wilting points, field capacity, and saturation. To be consistent with the corresponding units in the crop model, the original data in weight water content was converted into volume water content through the corresponding soil bulk density measurements. Winter wheat phenology observations include the date of emergence (more than 50% of the wheat seedlings in the field show the first green leaves and reached about 2 cm), anthesis (the inner and outer glumes of the middle and upper florets of more than 50% of the wheat ears in the whole field are open, and the anthers loose powder), and maturity (more than 80% of the wheat grains turn yellow, the glumes and stems turn yellow, and only the upper first and second nodes are still slightly green). In most cases, the phenological stage “anthesis” is missing. The anthesis date was calculated by adding seven days to the observed heading date (when more than 50% of the wheat in the whole field exposes the tip of the ear from the sheath of the flag leaf).County-level yield statistics dataThe county-level yield data was collected from city statistical yearbooks of the study area from 2007 to 2015. Since most statistical yearbooks do not directly record per-unit yield data, the county-level yield was obtained by dividing the total yield and planting area. It is worth noting that all yields were calculated in units of metric kilograms per cultivated hectares (kg·ha−1).The winter wheat land cover dataWe used a winter wheat land cover product from a 1 km resolution dataset named ChinaCropArea1km36. This data was derived from GLASS leaf area index products and crop phenology from 2000 to 2015. This dataset is the base map of our data production.The MODIS LAI dataWe used the improved 8-days MODIS LAI products (i.e., 1 km) generated by Yuan et al.32 to assimilate the WOFOST model. The products applied the modified temporal-spatial filter and Savitzky-Golay filter to overcome the spatial-temporal discontinuity and inconsistence of raw MODIS LAI products, which makes them more applicable for the realm of land surface and climate modeling. The products can be accessed via the Land-Atmosphere Interaction Research Group website at Sun Yat-sen University (http://globalchange.bnu.edu.cn/research/lai).The WOFOST model parameterization and calibrationThe WOFOST model introductionThe WOFOST model was initially developed as a crop growth simulation model to evaluate the yield potential of various crops in tropical countries37. In this study, we chose the WOFOST model because the model reaches a trade-off of the complexity of the crop model and is suitable for large-scale simulations3. The WOFOST model is a typical crop growth model that explains crop growth based on underlying processes such as photosynthesis and respiration and their response to changing environmental conditions38. The WOFOST model estimates phenology, LAI, aboveground biomass, and storage organ biomass (i.e., grain yield) at a daily time step39 (Fig. 2).Fig. 2Schematic overview of the major processes implemented in WOFOST. The Astronomical module calculates day length, some variables relating to solar elevation, and the fraction of diffuse radiation.Full size imageZonal parameterizationWe first divided the study area covered by AMS into seamless Thiessen polygon zones. Each Thiessen polygon contains only a single AMS. These zones represent the whole areas where any location is closer to its associated AMS point than any other AMS point. Then, we assigned parameters to the entire zone based on the AMS data, including crop calendar (date of emergence) and soil water retention parameters (soil moisture content at wilting point, field capacity, and saturation). Besides, we also optimized two main crop parameters for controlling phenological development stages, namely TSUM1 (accumulated temperature required from emergence to anthesis) and TSUM2 (accumulated temperature required from anthesis to maturity), by minimizing the cost function of the observational and simulated date corresponding to anthesis and maturity.Parameter calibration within a single zoneWe implemented the calibration of parameters within every single zone, as illustrated in Fig. 3. We calculated the average statistical yield of each county within every single zone from 2007 to 2015, then ranked the counties in descending order and divided them into three groups, namely high, medium, and low-level yield counties, by the 33% quantile and 67% quantile of the average statistical yield. The three counties corresponding to 17% quantile, 50% quantile, and 83% quantile would be used for subsequent calibration and represent the corresponding three yield level groups. We used the statistical yields (converted to dry matter mass based on the standard moisture content of 12.5%) of the three counties for multiple years and a harvest index for each province to convert the county-level yield to AGB for calibration. The harvest index of each province was mainly estimated from the AMS’s dynamic growth records on the biomass composition of the dominant winter wheat varieties of the province and a published literature40. Besides, we collected the maximum LAI observations on all agrometeorological stations in all years in the study area, according to its histogram. We found that the histogram follows a normal distribution with a mean of 6.5 and a standard deviation of 1.5. Finally, we calibrated three sets of parameters corresponding to three yield level groups in each single zone according to the three selected counties.Fig. 3Flow chart of parameter calibration within a single zone.Full size imageWe designed a three-step calibration strategy for a specific yield level group. Firstly, as winter wheat varieties did not change significantly according to information recorded by agrometeorological stations from 2007 to 2015, we assumed the crop parameters of winter wheat remain unchanged every three years to combine three years of observational data to calibrate the parameters of the WOFOST model better. We maximized a log-likelihood function based on the maximum LAI statistics and every three-year county-level yield and AGB data mentioned to optimize selected crop parameters (see Table S2 in the Supplement Materials).The log-likelihood function was constructed as follows:$$log;{{rm{L}}}_{{rm{LAI}}}=-frac{1}{2}left[dlogleft(2pi right)+logleft(left|{Sigma }_{{rm{LAI}}}right|right)+{rm{MD}}{left({{bf{x}}}_{{rm{LAI}}};{mu }_{{rm{LAI}}},{Sigma }_{{rm{LAI}}}right)}^{2}right]$$
(1)
$$log;{{rm{L}}}_{{rm{TWSO}}}=-frac{1}{2}left[dlog(2pi )+logleft(left|{{boldsymbol{Sigma }}}_{{rm{TWSO}}}right|right)+{rm{MD}}{left({{bf{x}}}_{{rm{TWSO}}};{{boldsymbol{mu }}}_{{rm{TWSO}}},{{boldsymbol{Sigma }}}_{{rm{TWSO}}}right)}^{2}right]$$
(2)
$$log;{{rm{L}}}_{{rm{AGB}}}=-frac{1}{2}left[dlog(2pi )+logleft(left|{{boldsymbol{Sigma }}}_{{rm{AGB}}}right|right)+{rm{MD}}{left({{bf{x}}}_{{rm{AGB}}};{{boldsymbol{mu }}}_{{rm{AGB}}},{{boldsymbol{Sigma }}}_{{rm{AGB}}}right)}^{2}right]$$
(3)
$$log;{rm{L}}=log;{L}_{{rm{LAI}}}+log;{L}_{{rm{TWSO}}}+log;{L}_{{rm{AGB}}}$$
(4)
Where log L is the natural logarithm of the likelihood function, d is the dimension, that is, the number of years of joint calibration, which is set to 3 in this study xLAI is the vector composed of the maximum value of the 3-year LAI simulated by WOFOST, μLAI and ΣLAI are the mean vector and error covariance matrix of maximum LAI based on observation statistics. The annual maximum LAI was assumed to be independent, and the mean and standard deviation for each year was set the same as the result of Fig. 3. Similarly, xTWSO and xAGB are the yield vector and AGB vector at maturity of 3 years simulated by WOFOST, and μTWSO, μAGB are their corresponding county-level statistic vector, ΣTWSO and ΣAGB are their corresponding error covariance matrix. In this study, we assumed that the annual yield or AGB was independent, and their corresponding standard deviation was 10% of their statistical value. |Σ| is the determinant of Σ. The expression ({rm{MD}}{({bf{x}};{boldsymbol{mu }},{boldsymbol{Sigma }})}^{2}={({bf{x}}-{boldsymbol{mu }})}^{{rm{T}}}{{boldsymbol{Sigma }}}^{-1}({bf{x}}-{boldsymbol{mu }})), where MD is the Mahalanobis distance between the point x and the mean vector μ.Secondly, we optimized the inter-annual irrigation. We optimized two parameters every year: the critical value of soil moisture (denoted as SMc) and the amount of irrigation (denoted as V). When the soil moisture simulated by WOFOST is lower than SMc, an irrigation event will be triggered, and the irrigation amount is V cm. In this study, we combined three-year data for calibration with six parameters for optimization. The optimization strategy is the same as the previous step by maximizing the log-likelihood function. Finally, we fixed the optimized irrigation parameters and repeated the first step to calibrate the selected crop parameters and obtain the final optimal parameters.Data assimilationConsidering that MODIS LAI is relatively low compared to the actual LAI of winter wheat41, we select a weak-constraint cost function based on the least square of normalized observational and simulated LAI as shown in Eq. (5), which is assimilating the trend information of MODIS LAI into the crop growth model.$$J={sum }_{{rm{t}}=1}^{{rm{n}}}{left(frac{{{rm{LAI}}}_{{rm{MODIS}}}^{{rm{t}}}-{{rm{LAI}}}_{{rm{MODIS}}}^{min}}{{{rm{LAI}}}_{{rm{MODIS}}}^{max}-{{rm{LAI}}}_{{rm{MODIS}}}^{min}}-frac{{{rm{LAI}}}_{{rm{WOFOS}}}^{{rm{t}}}-{{rm{LAI}}}_{{rm{WOFOS}}}^{min}}{{{rm{LAI}}}_{{rm{WOFOS}}}^{max}-{{rm{LAI}}}_{{rm{WOFOS}}}^{min}}right)}^{2}$$
(5)
Where ({{rm{LAI}}}_{{rm{MODIS}}}^{{rm{t}}}) and .. are MODIS LAI and WOFOST simulated LAI of time t. ({{rm{LAI}}}_{{rm{MODIS}}}^{max}) and ({{rm{LAI}}}_{{rm{WOFOS}}}^{max}) are maximum of MODIS LAI and WOFOST simulated LAI. ({{rm{LAI}}}_{{rm{MODIS}}}^{min}) and ({{rm{LAI}}}_{{rm{WOFOS}}}^{min}) are minimum of MODIS LAI and WOFOST simulated LAI. J is the value of the cost function.We reinitialize the day of emergence (IDEM), the life span of leaves growing at 35 °C (SPAN), and thermal time from emergence to anthesis (TSUM1) in the WOFOST model on each 1 km winter wheat pixel according to cost function between WOFOST LAI and MODIS LAI. Besides, we applied the Subplex algorithm from the NLOPT library (https://github.com/stevengj/nlopt) for parameter optimization. More

Plant materialsAn ALS-inhibitor-resistant johnsongrass (resistant to nicosulfuron) obtained from the University of Nebraska-Lincoln (source credit: Dr. John Lindquist) was used as the pollen source (male parent), and the natural johnsongrass population present in the experimental field at the Texas A&M University Farm, Somerville (Burleson County), Texas (30° 32′ 15.4″ N 96° 25′ 49.2″ W) with no history of ALS-inhibitor resistance was used as the pollen recipient (female parent). Prior to the initiation of the field experiment, the susceptibility to nicosulfuron of the natural johnsongrass population was verified by spraying Accent Q at the labeled field rate of 63 g ai ha−1 [mixed with 0.25% v/v Crop Oil Concentrate (COC)] on 10 randomly selected 1 m2 johnsongrass patches across the field area at 15–30 cm tall seedling stage. For this purpose, a CO2 pressurized backpack sprayer was calibrated to deliver 140 L ha−1 of spray volume at an operating speed of 4.8 kmph. The natural johnsongrass population was determined to be completely susceptible to nicosulfuron.During spring 2018, the seeds of AR johnsongrass were planted in pots (14-cm diameter and 12-cm tall) filled with potting soil mixture (LC1 Potting Mix, Sungro Horticulture Inc., Agawam, MA, USA) at the Norman Borlaug Center for Southern Crop Improvement Greenhouse Research Facility at Texas A&M University. The environmental conditions were set at 26/22 °C day/night temperature regime and a 14-h photoperiod. In each pot, 5 seeds were planted and thinned to one healthy seedling at 1-leaf stage. Seedlings were supplied with sufficient water and nutrients (Miracle-Gro Water Soluble All Purpose Plant Food, Scotts Miracle-Gro Products Inc., 14111 Scottslawn Road, Marysville, OH 43041). A total of 50 seedlings were established in the greenhouse and were maintained until they reached about 10 cm tall, at which point they were sprayed with 2× the field rate of nicosulfuron (63 × 2 = 126 g ai ha−1) (mixed with 0.25% v/v COC). The herbicide was applied using a track-sprayer (Research Track Sprayer, DeVries, Hollandale, MN) fitted with a flat fan nozzle (TeeJet XR110015) that was calibrated to deliver a spray volume of 140 L ha−1 at 276 kPa pressure, and at an operating speed of 4.8 kmph. All treated seedlings that survived the herbicide application at 21 days after treatment (DAT) were then used as the pollen donor in the field gene flow experiment. All plant materials were handled in accordance with relevant guidelines and regulations. No permissions or licenses were required for collecting the johnsongrass samples from the experimental fields.Dose–response assaysThe degree of resistance/susceptibility to nicosulfuron of the AR and AS johnsongrass biotypes were determined using a classical dose–response experiment. The assay consisted of seven rates (0, 0.0625, 0.125, 0.25, 0.5, 1, and 2×) for the AS population and nine rates (0, 0.25, 0.5, 1, 2, 4, 8, 16, and 32×) for the AR population [1 × (field recommended rate) = 63 g ai ha−1 of Accent Q]. The experimental units were arranged in a completely randomized design with four replications. Seeds of AR and AS plants were planted in plastic trays (25 × 25 cm) filled with commercial potting-soil mix (LC1 Potting Mix, Sungro Horticulture Inc., Agawam, MA, USA) and maintained at 26/22 °C day/night cycle with a 14-h photoperiod in the greenhouse. Seedlings at 1–2 leaf stage were thinned to 20 seedlings per tray; four replications each of 20 seedlings per dose were considered. The seedlings were watered and fertilized as needed. The assay was conducted twice, thus a total of 160 seedlings were screened for each dose.The established seedlings were sprayed with the appropriate herbicide dose at the 10–15 cm tall seedling stage. The herbicide was applied using a track sprayer calibrated to deliver a spray volume of 140 L ha−1 at 4.8 kmph operating speed. Survival (%) and injury (%) were assessed at 28 DAT. Any plant that failed to grow out of the herbicide impact was considered dead. Plant injury was rated for each plot (i.e. on the 20 seedlings per rep) on a scale of 0–100%, where 0 indicates no visible impact compared to the nontreated control and 100 indicates complete death of all plants in the tray. Immediately after the visual ratings were completed, shoot biomass produced by the 20 plants from each tray was determined by harvesting all the tissues at the soil level and drying them in an oven at 60 °C for 72 h. Seedling mortality data were used for fitting dose–response curves that allowed for determining the lethal dose that caused 100% mortality of the susceptible biotype. This dose was used as a discriminant dose to distinguish between a hybrid (that confers resistance to nicosulfuron as a result of gene flow) and a selfed progeny (susceptible to nicosulfuron) in the field gene flow study.Field experimental location and set-upThe field experiment was conducted across two ENVs in 2018 (summer and fall) and one in 2019 (fall) at the Texas A&M University Farm, Somerville (Burleson County), Texas (30° 32′ 15.4″ N 96° 25′ 49.2″ W). The study site is characterized by silty clay loam soil with an average annual rainfall of 98.2 cm. The field experiment followed the Nelder-wheel design40, i.e. concentric donor-receptor design, a widely used method for gene flow studies, wherein the pollen-donors are surrounded by the pollen-receptors (Fig. 1). In this study, the AR plants (planted in the central block of the wheel) served as the pollen-donors, whereas the AS plants (present in the spokes) served as the pollen-receptors.Figure 1Aerial view of the experimental arrangement that was used to quantify pollen-mediated gene flow from ALS-inhibitor resistant (AR) to -susceptible (AS) johnsongrass at the Texas A&M University Research Farm near College Station, Texas. AR johnsongrass plants were transplanted in the pollen-donor block of 5 m diameter at the center of the field. The surrounding pollen-receptor area was divided into four cardinal (N, E, S, W) and four ordinal (NE, SE, SW, NW) directional blocks where naturally-existing AS johnsongrass plants were used as the pollen-recipients. AS panicles exhibiting flowering synchrony with AR plants were tagged at specific distances (5–50 m, at 5 m increments) along the eight directional arms. A tall-growing biomass sorghum border was established in the perimeter of the experimental site to prevent pollen inflow from outside areas.Full size imageThe center of the wheel was 5 m in diameter, and each spoke was 50 m long starting at the periphery of the central circular block. Thirty AR plants (pollen-donors) were transplanted in four concentric rings of 1, 5, 9, and 15 plants in the 5 m diameter central block, surrounded by the pollen-receptors (i.e. AS plants) (Fig. 1). The AR plants were contained within the central block during the 2 years of the field experiment by harvesting and removing all mature seeds and removing any expanding rhizomatous shoots. Further, field cultivation was completely avoided in the central block throughout the study period. Any newly emerging johnsongrass plants (seedling/rhizomatous) other than the transplanted AR plants in the central block were removed periodically by manual uprooting.The wheel consisted of eight spokes (i.e. directional blocks) arranged in four cardinal (N, E, S, W) and four ordinal (NE, SE, NW, SW) directions (Fig. 1). The plots to quantify gene flow frequency were arranged at 0 (border of the central block), 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 m distances from the central block in all eight directions (Fig. 1). Each plot measured 3 × 2 m and the area surrounding the plots was shredded prior to the booting stage with a Rhino® RC flail shredder (RHINOAG, INC., Gibson City, IL 60936).A tall-growing biomass sorghum border (6 m wide) was established surrounding the experimental area in all directions to prevent potential inflow of pollen from other Sorghum spp. in the nearby areas. Additionally, prevailing weather conditions, specifically wind direction, wind speed, relative humidity, and air temperature measured at 5-min intervals were obtained from a nearby weather station located within the Texas A&M research farm (http://afs102.tamu.edu/). The field did not require any specific agronomic management in terms of irrigation, fertilization, or pest management.Flowering synchrony, tagging, and seed harvestingAt peak flowering, when  > 50% of the plants in the AR block started anther dehiscence (i.e., pollen shedding), ten AS panicles (five random plants × 2 panicles per plant) that showed flowering synchrony with AR plants and displayed protruded, receptive stigma were tagged using colored ribbons at each distance and direction. At seed maturity, the tagged AS panicles were harvested separately for each distance and direction. Panicles were threshed, seeds were cleaned manually, and stored under room conditions until used in the herbicide resistance screening to facilitate after-ripening and dormancy release.Resistance screeningThe hybrid progeny produced on AS plants as a result of outcrossing with AR plants would be heterozygous for the allele harboring nicosulfuron resistance, and would exhibit survival upon exposure to the herbicide applied at the discriminant dose at which all wild type (AS) plants would die. The discriminant dose was determined using the dose–response study described above. Thus, the frequency of resistant plants in the progeny would represent outcrossing/gene flow (%).To effectively detect the levels of gene flow from AR to AS biotypes especially at low frequencies, the minimum sample size required for resistance screening was determined based on the following formula (Eq. 1)41:$${text{N }} = {text{ ln}}left( {{1} – P} right)/{text{ln}}left( {{1} – p} right),$$
(1)
where P is the probability of detecting a resistant progeny in the least frequent class and p is the probability of the least frequent class. Based on this formula, a minimum of 298 to as high as 916 plants were screened for each distance within each direction, allowing for a 1% detection level (p = 0.01) with a 95% (P = 0.95) confidence interval.Approximately one-year old progeny seeds harvested from the AS plants were scarified using a sandpaper for 15–20 s to release dormancy. The seeds for each distance within each direction were planted in four replicates of plastic trays (50 × 25 cm) filled with potting soil mixture (LC1 Potting Mix, Sungro Horticulture Inc., Agawam, MA, USA). The plants were raised at the Norman Borlaug Center for Southern Crop Improvement Greenhouse Research Facility at Texas A&M University. The greenhouse was maintained at 28/24 °C day/night temperature regime and a 14-h photoperiod. About 10–15 cm tall seedlings were sprayed with the discriminant dose of the ALS-inhibitor nicosulfuron (Accent Q, 95 g ai ha−1) using a spray chamber (Research Track Sprayer, DeVries, Hollandale, MN) fitted with a flat fan nozzle (TeeJet XR110015) that was calibrated to deliver a spray volume of 140 L ha−1 at 276 kPa pressure, operating at a speed of 4.8 kmph. At 28 DAT, percent seedling survival was determined based on the number of plants that survived the herbicide application out of the total number of plants sprayed. The number of plants in each tray was counted before spraying.Molecular confirmation of hybridsLeaf tissue samples were collected from thirty random surviving plants (putative resistant) in the herbicide resistance screening study for each of the three field ENVs, thus totaling 90 samples. Genomic DNA was extracted from 100 mg of young leaf tissue using the modified CTAB protocol42. The concentration of DNA was determined using a Nanodrop 1000 UV–Vis spectrophotometer (DeNovix DS-II spectrophotometer, DeNovix Inc., Wilmington, DE 19810, USA). DNA was then diluted to a concentration of 20 ng/µl for PCR assay. The nicosulfuron-resistant johnsongrass from Nebraska used in this study possessed the Trp574Leu mutation39. Hence, single nucleotide polymorphism (SNP) primers targeting a unique short-range haplotype of Inzen® sorghum (Val560Ile + Trp574Leu) were performed using the PCR Allele Competitive Extension (PACE) platform to confirm the resistant plants43. The SNP primers and the PACE genotyping master mix were obtained from Integrated DNA Technologies (IDT) Inc. (Coralville, IA) and 3CR Bioscience (Harlow CM20 2BU, United Kingdom), respectively. In addition to the two no-template controls (NTCs), two nicosulfuron-resistant johnsongrass, one wild-type johnsongrass, and one Inzen® sorghum were also used in the PCR.The PCR was performed according to the manufacturer’s protocol (Bio-Rad Laboratories, Inc., Hercules, CA), with denaturation for 15 min at 94 °C, followed by 10 cycles of denaturation at 94 °C for 20 s, annealing and extension at 65–57 °C for 60 s, 30 cycles of denaturation for 20 s at 94 °C, and annealing and extension for 60 s at 57 °C. Fluorescence of the reaction products were detected using a BMG PHERAStar plate reader that uses the FAM (fluorescein amidite) and HEX (hexachloro-fluorescein) fluorophores.Data analysisFor the dose–response assay, three-parameter sigmoidal curves (Eq. 2) were fit on the seedling mortality data for the AS and AR biotypes (with log of herbicide doses), using SigmaPlot version 14.0 (Systat Software Inc., San Jose, CA).$$y=b/[1+{exp}^{left(-(x-eright)/c)}],$$
(2)
where, y is the mortality (%), x is the herbicide dose (g ai ha−1), b is the slope around e, c is the lower limit (theoretical minimum for y normalized to 0%), and e = LD50 (inflection point, mid-point or estimated herbicide dose when y = 50%). Windrose plots that represented wind speed and frequency during the flowering window in each of the eight directions were created using a macro in Microsoft Excel. Progeny seedling survival (%) that represents gene flow (%) was determined using Eq. (3).$${text{PMGF }}left( {text{%}} right){ } = { }left( frac{X}{Y} right)_{{i,j{ }}} times { }100,$$
(3)
where, X is the number of plants that survived the herbicide application, Y is the total number of plants sprayed for ith distance in jth direction.To test whether gene flow frequencies varied among the directions, ANOVA was conducted using JMP PRO v.14 (SAS Institute, Cary, NC, USA), based on the average gene flow frequency values in each direction; ENVs were considered as replicates in this analysis. A non-linear regression analysis for gene flow rate, describing an exponential decay function (Eq. 4), was fit using SigmaPlot based on the gene flow frequencies observed at different distances pooled across the directions and ENVs.$$y=y0+left[atimes {exp}^{left(-btimes xright)}right],$$
(4)
where, y is the PMGF (%), x is the distance (m) from pollen source, y0 is the lower asymptote (theoretical minimum for y normalized to 0%), a is the inflection point, mid-point or estimated distance when y = 50%, and b is the slope around a.A Pearson correlation analysis was conducted to determine potential association between PMGF [overall PMGF, short-distance PMGF (5 m), and long-distance PMGF (50 m)] and the environmental parameters temperature, relative humidity, and dew point. Further, a correlation analysis was also conducted to understand the association between PMGF frequencies and specific wind parameters such as wind frequency, wind speed, and gust speed. The molecular data were analyzed using KlusterCaller 1.1 software (KBioscience). More

Reference data collectionIn February 2019, we involved forest experts from different regions around the world and organized a workshop to (1) discuss the variety of forest management practices that take place in various parts of the world; (2) explore what types of forest management information could be collected by visual interpretation of very high-resolution images from Google Maps and Microsoft Bing Maps, in combination with Sentinel time series and Normalized Difference Vegetation Index (NDVI) profiles derived from Google Earth Engine (GEE); (3) generalize and harmonize the definitions at global scale; (4) finalize the Geo-Wiki interface for the crowdsourcing campaigns; and (5) build a data set of control points (or the expert data set), which we used later to monitor the quality of the crowdsourced contributions by the participants. Based on the results of this analysis, we launched the crowdsourcing campaigns by involving a broader group of participants, which included people recruited from remote sensing, geography and forest research institutes and universities. After the crowdsourcing campaigns, we collected additional data with the help of experts. Hence, the final reference data consists of two parts: (1) a randomly stratified sample collected by crowdsourcing (49,982 locations); (2) a targeted sample collected by experts (176,340 locations, at those locations where the information collected from the crowdsourcing campaign was not large enough to ensure a robust classification).DefinitionsTable 1 contains the initial classification used for visual interpretation of the reference samples and the aggregated classes presented in the final reference data set. For the Geo-Wiki campaigns, we attempted to collect information (1) related to forest management practices and (2) recognizable from very high-resolution satellite imagery or time series of vegetation indices. The final reference data set and the final map contain an aggregation of classes, i.e., only those that were reliably distinguishable from visual interpretation of satellite imagery.Table 1 Forest management classes and definitions.Full size tableSampling design for the crowdsourcing campaignsInitially, we generated a random stratified sample of 110,000 sites globally. The total number of sample sites was chosen based on experiences from past Geo-Wiki campaigns12, a practical estimation of the potential number of volunteer participants that we could engage in the campaign, and the expected spatial variation in forest management. We used two spatial data sets for the stratification of the sample: World Wildlife Fund (WWF) Terrestrial Ecoregions13 and Global Forest Change14. The samples were stratified into three biomes, based on WWF Terrestrial Ecoregions (Fig. 2): boreal (25 000 sample sites), temperate (35,000 sample sites) and tropical (50,000 sample sites). Within each biome, we used Hansen’s14 Global Forest Change maps to derive areas with “forest remaining forest” 2000–2015, “forest loss or gain”, and “permanent non-forest” areas.Fig. 2Biomes for sampling stratification (1 – boreal, 2 – temperate, 3 – sub-tropical and tropical).Full size imageThe sample size was determined from previous experiences, taking into account the expected spatial variation in forest management within each biome. Tropical forests had the largest sample size because of increasing commodity-driven deforestation15, the wide spatial extent of plantations, and slash and burn agriculture. Temperate forests had a larger sample compared to boreal forests due to their higher fragmentation. Each sample site was classified by at least three different participants, thus accounting for human error and varying expertise16,17,18. At a later stage, following a preliminary analysis of the data collected, we increased the number of sample sites to meet certain accuracy thresholds for every mapped class (aiming to exceed 75% accuracy).The Geo‐Wiki applicationGeo‐Wiki.org is an online application for crowdsourcing and expert visual interpretation of satellite imagery, e.g., to classify land cover and land use. This application has been used in several data collection campaigns over the last decade16,19,20,21,22,23. Here, we implemented a new custom branch of Geo‐Wiki (‘Human impact on Forest’), which is devoted to the collection of forest management data (Fig. 3). Various map overlays (including satellite images from Google Maps, Microsoft Bing Maps and Sentinel 2), campaign statistics and tools to aid interpretation, such as time series profiles of NDVI, were provided as part of this Geo‐Wiki branch, giving users a range of options and choices to facilitate image classification and general data collection. Google Maps and Microsoft Bing Maps include mosaics of very high-resolution satellite and aerial imagery from different time periods and multiple image providers, including the Landsat satellites operated by NASA and USGS as base imagery to commercial image providers such as Digital Globe. More information on the spatial and temporal distribution of very high-resolution satellite imagery can be found in Lesiv et al.24. This collection of images was supplied as guidance for visual interpretation16,20. Participants could analyze time series profiles of NDVI from Landsat, Sentinel 2 and MODIS images, which were derived from Google Earth Engine (GEE). More information on tools can be found in Supplementary file 1.Fig. 3Screenshot of the Geo‐Wiki interface showing a very high-resolution image from Google Maps and a sample site as a 100 mx100 m blue square, which the participants classified based on the forest management classes on the right.Full size imageThe blue box in Fig. 3 corresponds to 100 m × 100 m pixels aligned with the Sentinel grid in UTM projection. It is the same geometry required for the classification workflow that is used to produce the Copernicus Land Cover product for 201511.Before starting the campaign, the participants were shown a series of slides designed to help them gain familiarity with the interface and to train them in how to visually determine and select the most appropriate type of land use and forest management classes at each given location, thereby increasing both consistency and accuracy of the labelling tasks among experts. Once completed, the participants were shown random locations (from the random stratified sample) on the Geo‐Wiki interface and were then asked to select one of the forest management classes outlined in the Definition section (see Table 1 above).Alternatively, if there was either insufficient quality in the available imagery, or if a participant was unable to determine the forest management type, they could skip such a site (Fig. 3). If a participant skipped a sample site because it was too difficult, other participants would then receive this sample site for classification, whereas in the case of the absence of high-resolution satellite imagery, i.e., Google Maps and Microsoft Bing Maps, this sample site was then removed from the pool of available sample sites. The skipped locations were less than 1% of the total amount of locations assigned for labeling. Table 2 shows the distribution of the skipped locations by countries, based on the subset of the crowdsourced data where all the participants agreed.Table 2 Distribution of the skipped locations by countries.Full size tableQuality assurance and data aggregation of the crowdsourced dataBased on the experience gained from previous crowdsourcing campaigns12,19, we invested in the training of the participants (130 persons in total) and overall quality assurance. Specifically, we provided initial guidelines for the participants in the form of a video and a presentation that were shown before the participants could start classifying in the forest management branch (Supplementary file 1). Additionally, the participants were asked to classify 20 training samples before contributing to the campaign. For each of these training samples, they received text‐based feedback regarding how each location should be classified. Summary information about the participants who filled in the survey at the end of the campaign (i.e., gender, age, level of education, and their country of residence) is provided in the Supplementary file 2. We would like to note that 130 participants is a high number, especially taking the complexity of the task into consideration.Furthermore, during the campaign, sample sites that were part of the “control” data set were randomly shown to the participants. The participants received text-based feedback regarding whether the classification had been made correctly or not, with additional information and guidance. By providing immediate feedback, our intention was that participants would learn from their mistakes, increasing the quality and classification accuracy over time. If the text‐based feedback was not sufficient to provide an understanding of the correct classification, the participants were able to submit a request (“Ask the expert”) for a more detailed explanation by email.The control set was independent of the main sample, and it was created using the same random stratified sampling procedure within each biome and the stratification by Global Forest Change maps14 (see “Sample design” section). To determine the size of the control sample, we considered two aspects: (a) the maximum number of sample sites that one person could classify during the entire campaign; (b) the frequency at which control sites would appear among the task sites (defined at 15%, which is a compromise between the classification of as many unknown locations as possible and a sufficient level of quality control, based on previous experience). Our control sample consisted of 5,000 sites. Each control sample site was classified twice by two different experts. When the two experts agreed, these sample sites were added to the final control sample. Where disagreement occurred (in 25% of cases), these sample sites were checked again by the experts and revised accordingly. During the campaign, participants had the option to disagree with the classification of the control site and submit a request with their opinion and arguments. They received an additional quality score in the situation when they were correct, but the experts were not. This procedure also ensured an increase in the quality of the control data set.To incentivize participation and high-quality classifications, we offered prizes as part of the campaign design. The ranking system for the prize competition considered both the quality of the classifications and the number of classifications provided by a participant. The quality measure was based on the control sample discussed above. The participants randomly received a control point, which was classified in advance by the experts. For every control point, a participant could receive a maximum of +30 points (fully correct classification) to a minimum of −30 points (incorrect classification). In the case where the answer was partly correct (e.g., the participant correctly classified that the forest is managed, but misclassified the regeneration type), they received points ranging from 5 to 25.The relative quality score for each participant was then calculated as the total sum of gained points divided by the maximum sum of points that this participant could have earned. For any subsequent data analysis, we excluded classifications from those participants whose relative quality score was less than 70%. This threshold corresponds to an average score of 10 points at each location (out of a maximum of 30 points), i.e., where participants were good at defining the aggregated forest management type but may have been less good at providing the more detailed classification.Unfortunately, we observed some imbalance in the proportion of participants coming from different countries, e.g. there were not so many participants from the tropics. This could have resulted in interpretation errors, even when all the participants agreed on a classification. To address this, we did an additional quality check. We selected only those sample sites where all the participants agreed and then randomly checked 100 sample sites from each class. Table 3 summarizes the results of this check and explains the selection of the final classes presented in Table 1.Table 3 Qualitative analysis of the reference sample sites with full agreement.Full size tableAs a result of the actions outlined in Table 3, we compiled the final reference data set, which consisted of 49,982 consistent sample sites.Additional expert data collectionWe used the reference data set to produce a test map of forest management (the classification algorithm used is described in the next section). By checking visually and comparing against the control data set, we found that the map was of insufficient quality for many locations, especially in the case of heterogeneous landscapes. While several reasons for such an unsatisfactory result are possible, the experts agreed that a larger sample size would likely increase the accuracy of the final map, especially in areas of high heterogeneity and for forest management classes that only cover a small spatial extent. To increase the amount of high-quality training data and hence to improve the map, we collected additional data using a targeted approach. In practice, the map was uploaded to Geo-Wiki, and using the embedded drawing tools, the experts randomly checked locations on the map, focusing on their region of expertise and added classified polygons in locations where the forest management was misclassified. To limit model overfitting and oversampling of certain classes, the experts also added points for correctly mapped classes to keep the density of the points the same. This process involved a few iterations of collecting additional points and training the classification algorithm until the map accuracy reached 75%. In total, we collected an additional 176,340 training points. With the 49,982 consistent training points from the Geo-Wiki campaigns, this resulted in 226,322 (Fig. 4). This two-pronged approach would not have been possible without the exhaustive knowledge obtained from running the initial Geo-Wiki campaigns, including numerous questions raised by the campaign participants. Figure 4 also highlights in yellow the areas of very high sampling density, I.e., those collected by the experts. The sampling intensity of these areas is much higher in comparison with the randomly distributed crowdsourced locations, and these are mainly areas with very mixed forest classes or small patches, in most cases, including plantations.Fig. 4Distribution of reference locations.Full size imageClassification algorithmTo produce the forest management map for the year 2015, we applied a workflow that was developed as part of the production of the Copernicus Global Land Services land cover at 100 m resolution (CGLS-LC100) collection 2 product11. A brief description of the workflow (Fig. 5), focusing on the implemented changes, is given below. A more thorough explanation, including detailed technical descriptions of the algorithms, the ancillary data used, and the intermediate products generated, can be found in the Algorithm Theoretical Basis Document (ATBD) of the CGLS-LC100 collection 2 product25.Fig. 5Workflow overview for the generation of the Copernicus Global Land Cover Layers. Adapted from the Algorithm Theoretical Basis Document25.Full size imageThe CGLS-LC100 collection 2 processing workflow can be applied to any satellite data, as it is unspecific to different sensors or resolutions. While the CGLS-LC100 Collection 2 product is based on PROBA-V sensor data, the workflow has already been tested with Sentinel 2 and Landsat data, thereby using it for regional/continental land cover (LC) mapping applications11,26. For generating the forest management layer, the main Earth Observation (EO) input was the PROBA-V UTM Analysis Ready Data (ARD) archive based on the complete PROBA-V L1C archive from 2014 to 2016. The ARD pre-processing included geometric transformation into a UTM coordinate system, which reduced distortions in high northern latitudes, as well as improved atmospheric correction, which converted the Top-of-Atmosphere reflectance to surface reflectance (Top-of-Canopy). In a further processing step, gaps in the 5-daily PROBA-V UTM multi-spectral image data with a Ground Sampling Distance (GSD) of ~0.001 degrees (~100 m) were filled using the PROBA-V UTM daily multi-spectral image data with a GSD of ~0.003 degrees (~300 m). This data fusion is based on a Kalman filtering approach, as in Sedano et al.27, but was further adapted to heterogonous surfaces25. Outputs from the EO pre-processing were temporally cleaned by using the internal quality flags of the PROBA-V UTM L3 data, a temporal cloud and outlier filter built on a Fourier transformation. This was done to produce consistent and dense 5-daily image stacks for all global land masses at 100 m resolution and a quality indicator, called the Data Density Indicator (DDI), used in the supervised learning process of the algorithm.Since the total time series stack for the epoch 2015 (a three-year period including the reference year 2015 +/− 1 year) would be composed of too many proxies for supervised learning, the time and spectral dimension of the data stack had to be condensed. The spectral domain was condensed by using Vegetation Indices (VIs) instead of the original reflectance values. Overall, ten VIs based on the four PROBA-V reflectance bands were generated, which included: Normalized Difference Vegetation Index (NDVI); Enhanced Vegetation Index (EVI); Structure Intensive Pigment Index (SIPI); Normalized Difference Moisture Index (NDMI); Near-Infrared reflectance of vegetation (NIRv); Angle at NIR; HUE and VALUE of the Hue Saturation Value (HSV) color system transformation. The temporal domain of the time series VI stacks was then condensed by extracting metrics, which are used as general descriptors to enable distinguishing between the different LC classes. Overall, we extracted 266 temporal, descriptive, and textual metrics from the VI times series stacks. The temporal descriptors were derived through a harmonic model, fitted through the time series of each of the VIs based on a Fourier transformation28,29. In addition to the seven parameters of the harmonic model that describe the overall level and seasonality of the VI time series, 11 descriptive statistics (mean, standard deviation, minimum, maximum, sum, median, 10th percentile, 90th percentile, 10th – 90th percentile range, time step of the first minimum appearance, and time step of the first maximum appearance) and one textural metric (median variation of the center pixel to median of the neighbours) were generated for each VI. Additionally, the elevation, slope, aspect, and purity derived at 100 m from a Digital Elevation Model (DEM) were added. Overall, 270 metrics were extracted from the PROBA-V UTM 2015 epoch.The main difference to the original CGLS-LC100 collection 2 algorithms is the use of forest management training data instead of the global LC reference data set, as well as only using the discrete classification branch of the algorithm. The dedicated regressor branch of the CGLS-LC100 collection 2 algorithm, i.e., outputting cover fraction maps for all LC classes, was not needed for generating the forest management layer.In order to adapt the classification algorithm to sub-continental and continental patterns, the classification of the data was carried out per biome cluster, with the 73 biome clusters defined by the combination of several global ecological layers, which include the ecoregions 2017 dataset30, the Geiger-Koeppen dataset31, the global FAO eco-regions dataset32, a global tree-line layer33, the Sentinel-2 tiling grid and the PROBA-V imaging extent;30,31 this, effectively, resulted in the creation of 73 classification models, each with its non-overlapping geographic extent and its own training dataset. Next, in preparation for the classification procedure, the metrics of all training points were analyzed for outliers, as well as screened via an all-relevant feature selection approach for the best metric combinations (i.e., best band selection) for each biome cluster in order to reduce redundancy between parameters used in the classification. The best metrics are defined as those that have the highest separability compared to other metrics. For each metric, the separability is calculated by comparing the metric values of one class to the metric values of another class; more details can be found in the ATBD25. The optimized training data set, together with the quality indicator of the input data (DDI data set) as a weight factor, were used in the training of the Random Forest classifier. Moreover, a 5-fold cross-validation was used to optimize the classifier parameters for each generated model (one per biome).Finally, the Random Forest classification was used to produce a hard classification, showing the discrete class for each pixel, as well as the predicted class probability. In the last step, the discrete classification results (now called the forest management map) are modified by the CGLS-LC100 collection 2 tree cover fraction layer29. Therefore, the tree cover fraction layer, showing the relative distribution of trees within one pixel, was used to remove areas with less than 10% tree cover fraction in the forest management layer, following the FAO definition of forest. Figure 6 shows the class probability layer that illustrates the model behavior, highlighting the areas of class confusion. This layer shows that there is high confusion between forest management classes in heterogeneous landscapes, e.g., in Europe and the Tropics while homogenous landscapes, such as Boreal forests, are mapped with high confidence. It is important to note that a low probability does not mean that the classification is wrong.Fig. 6The predicted class probability by the Random Forest classification.Full size image More

Frelich, L. E. Forest Dynamics and Disturbance Regimes, Study from Every Green and Deciduous Temperate Forest 287 (Cambridge University Press, 2002).Book 

Google Scholar 
Hadley, K. S. The role of disturbance, topography, and forest structure in the development of a montane forest landscape. J. Torrey Bot. Soc. 121(1), 47–61 (1994).Article 

Google Scholar 
Gracia, M., Montane, F., Pique, J. & Retana, J. Overstory structure and topographic gradients determining diversity and abundance of understory shrub species in temperate forests in central Pyrenees (NE Spain). For. Ecol. Manag. 242, 391–397 (2007).Article 

Google Scholar 
Scheller, R. M. & Mladenoff, D. J. Understory species patterns and diversity in old-growth and managed northern hardwood forests. Ecol. Appl. 12(5), 1329–1343 (2002).Article 

Google Scholar 
Sagheb-Talebi, K., Sajedi, T. & Pourhashemi, M. Forest of Iran, a Treasure from the Past, a Hope for the Future 145 (Springer, 2014).
Google Scholar 
Homami Totmaj, L., Alizadeh, K., Giahchi, P., Darvishi Khatooni, J. & Behling, H. Late Holocene Hyrcanian forest and environmental dynamics in the mid-elevated highland of the Alborz Mountains, northern Iran. Rev. Palaeobot. Palynol. 295, 104507 (2021).Article 

Google Scholar 
Vakili, M. et al. Resistance and resilience of Hyrcanian mixed forests under natural and anthropogenic disturbances. Front. For. Glob. Change 4, 98 (2021).Article 

Google Scholar 
Aguirre, O., Hui, G., von Gadow, K. & Jiménez, J. An analysis of spatial forest structure using neighbourhood-based variables. For. Ecol. Manag. 183(1–3), 137–145 (2003).Article 

Google Scholar 
Li, Y., Hui, G., Zhao, Z., Hu, Y. & Ye, S. Spatial structural characteristics of three hardwood species in Korean pine broad-leaved forest—Validating the bivariate distribution of structural parameters from the point of tree population. For. Ecol. Manag. 314, 17–25 (2014).Article 

Google Scholar 
Condit, R. et al. Spatial patterns in the distribution of tropical tree species. Science 288(5470), 1414–8 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Lü, C. et al. Population structure and spatial patterns of Haloxylon ammodendron population along the northwestern edge of Junggar basin. J. Desert Res. 32, 380–387 (2012).ADS 

Google Scholar 
Fazlollahi Mohammadi, M., Jalali, S. G., Kooch, Y. & Theodose, T. A. The influence of landform on the understory plant community in a temperate Beech forest in northern Iran. Ecol. Res. 30, 385–394 (2015).Article 

Google Scholar 
Fazlollahi Mohammadi, M., Jalali, S. G., Kooch, Y. & Said-Pullicino, D. Slope Gradient and Shape Effects on Soil Profiles in the Northern Mountainous Forests of Iran. Euras. Soil Sci. 49(12), 1366–1374 (2016).ADS 
Article 

Google Scholar 
Fazlollahi Mohammadi, M., Jalali, S. G., Kooch, Y. & Said-Pullicino, D. The effect of landform on soil microbial activity and biomass in a Hyrcanian oriental beech stand. CATENA 149, 309–317 (2017).CAS 
Article 

Google Scholar 
Fazlollahi Mohammadi, M., Jalali, S. G., Kooch, Y. & Theodose, T. A. Tree species composition biodiversity and regeneration in response to catena shape and position in a mountain forest. Scand. J. For. Res. 32(1), 80–90 (2017).Article 

Google Scholar 
Harms, K. E., Condit, R., Hubbell, S. P. & Foster, R. B. Habitat association of tree and shrubs in a 50-ha neotropical forest plot. J. Ecol. 89, 947–959 (2001).Article 

Google Scholar 
Gunatilleke, C. V. S. et al. Species-habitat associations in a Sri Lank and ipterocap forest. J. Trop. Ecol. 22, 371–378 (2006).Article 

Google Scholar 
Rubino, D. L. & McCarthy, B. C. Evaluation of coarse woody debris and forest vegetation across topographic gradients in a southern Ohio forest. For. Ecol. Manag. 183, 221–238 (2003).Article 

Google Scholar 
Mohsennezhad, M., Shokri, M., Zal, H. & Jafarian, Z. The effects of soil properties and physiographic factors on plant communities distribution in Behrestagh Rangeland. Rangeland 4(2), 262–275 (2010).
Google Scholar 
Sefidi, K., Esfandiary Darabad, F. & Azaryan, M. Effect of topography on tree species composition and volume of coarse woody debris in an Oriental beech (Fagus orientalis Lipsky) old growth forests, northern Iran. IFOREST Biogeosci. For. 9, 658–665 (2016).Article 

Google Scholar 
Valipour, A. et al. Relationships between forest structure and tree’s dimensions with physiographical factors in Armardeh forests (Northern Zagros). Iran. J. For. Poplar Res. 21(1), 30–47 (2013).
Google Scholar 
Clark, P. J. & Evans, F. C. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35, 445–453 (1954).Article 

Google Scholar 
Naqinezhad, A. et al. The combined effects of climate and canopy cover changes on understorey plants of the Hyrcanian forest biodiversity hotspot in northern Iran. Glob. Change Biol. 28(3), 1103–1118 (2022).Article 

Google Scholar 
Pelissaria, A. L. et al. Geostatistical modeling applied to spatiotemporal dynamics of successional tree species groups in a natural Mixed Tropical Forest. Ecol. Indic. 78, 1–7 (2017).Article 

Google Scholar 
Pretzsch, H. & Zenner, E. K. Toward managing mixed-species stands: From parametrization to prescription. For. Ecosyst. 4, 19 (2017).Article 

Google Scholar 
Yousefi, S. et al. Spatio-temporal variation of throughfall in a hyrcanian plain forest stand in Northern Iran. J. Hydrol. Hydromech. 66(1), 97–106 (2018).Article 

Google Scholar 
Soil Survey Staff. Keys to Soil Taxonomy 12th edn. (USDA-Natural Resources Conservation Service, 2014).
Google Scholar 
Land Info, L. L. C. http://www.landinfo.com/country-iran.html. Accessed (2013).Beven, K. J. & Kirkby, M. J. A. Physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du basin versant. Hydrol. Sci. J. 24(1), 43–69 (1979).Article 

Google Scholar 
Bourgeron, P. S. Spatial aspects of vegetation structure. In Ecosystems of the World 14A—Tropical Rain Forest Ecosystems, Structure and Function (ed. Golley, F. B.) 29–47 (Elsevier, 1983).
Google Scholar 
Moeur, M. Characterizing spatial patterns of trees using stem-mapped data. For. Sci. 39(4), 756–775 (1993).ADS 

Google Scholar 
Chokkalingam, U. & White, A. Structure and spatial patterns of trees in old-growth northern hardwood and mixed forests of northern Maine. Plant Ecol. 156(2), 139–160 (2001).Article 

Google Scholar 
Ferhat, K. A. R. A. Spatial patterns of longleaf pine (Pinus palustris Mill.): A case study. Euras. J. For. Sci. 9(3), 151–159 (2021).
Google Scholar 
Pommerening, A. Approaches to quantifying forest structures. Forestry 75(3), 305–324 (2002).Article 

Google Scholar 
Pommerening, A. & Särkkä, A. What mark variograms tell about spatial plant interactions. Ecol. Model. 251, 64–72 (2013).Article 

Google Scholar 
Goovaerts, P. Geostatistical tools for characterizing the spatial variability of microbiological and physico-chemical soil properties. Biol. Fertil. Soils. 27, 315–334 (1998).CAS 
Article 

Google Scholar 
Landim, P. M. B. & Sturaro, J. R. Krigagem indicativa aplicada à elaboração de mapas probabilísticos de riscos. Geomatematica, Texto didático, 6. DGA, IGCE, Universidade Estadual de São Paulo (UNESP), Rio Claro, São Paulo, Brazil. Available at: http://www.rc.unesp.br/igce/aplicada/textodi.html. Accessed 25/05/13 (2002).Deutsch, C. V. & Journel, A. G. GSLIB: Geostatistical Software Library and User’s Guide 119 (Oxford University Press, 1992).
Google Scholar 
Oliver, M. A. & Webster, R. Combining nested and linear sampling for determining the scale and form of spatial variation of regionalized variables. Geogr. Anal. 18, 227–242 (1986).Article 

Google Scholar 
Zhao, Z., Ashraf, M. I. & Meng, F. R. Model prediction of soil drainage classes over a large area using a limited number of field samples: A case study in the province of Nova Scotia, Canada. Can. J. Soil Sci. 93(1), 73–83 (2013).Article 

Google Scholar 
Brubaker, S. C., Jones, A. J., Lewis, D. T. & Frank, K. Soil properties associated with landscape position. Soil Sci. Soc. Am. J. 57, 235–239 (1993).ADS 
Article 

Google Scholar 
Bellingham, P. J. & Tanner, E. V. J. The influence of topography on tree growth, mortality, and recruitment in a tropical Montane Forest. Biotropica 32(3), 378–384 (2000).Article 

Google Scholar 
Luizao, R. C. C. et al. Variation of carbon and nitrogen cycling processes along a topographic gradient in a Central Amazonian forest. Glob. Change Biol. 10, 592–600 (2004).ADS 
Article 

Google Scholar 
Beaty, R. M. & Taylor, A. H. Spatial and temporal variation of fire regimes in a mixed conifer forest landscape, southern cascades, California, USA. J. Biogeogr. 28, 955–966 (2001).Article 

Google Scholar 
Castilho, C. V. et al. Variation in aboveground tree live biomass in a central Amazonian Forest: Effects of soil and topography. For. Ecol. Manag. 234, 85–96 (2006).Article 

Google Scholar 
Swanson, F. J., Kratz, T. K., Caine, N. & Woodmansee, R. G. Landform effects on eco-system patterns and processes. Biol. Sci. 38, 92–98 (1988).
Google Scholar 
Kooch, Y., Hosseini, S. M., Mohammadi, J. & Hojjati, S. M. Windthrow effects on biodiversity of natural forest ecosystem in local scale. Hum. Environ. 9(3), 65–72 (2011).
Google Scholar 
Köhl, M. & Gertner, G. Geostatistics in evaluating forest damage surveys: Considerations on methods for describing spatial distributions. For. Ecol. Manag. 95(2), 131–140 (1997).Article 

Google Scholar 
Habashi, H., Hosseini, S. M., Mohammadi, J. & Rahmani, R. Stand structure and spatial pattern of trees in mixed Hyrcanian beech forests of Iran. Iran. J. For. Poplar Res. 15(1), 64–55 (2007).
Google Scholar 
Von Oheimb, G., Westphal, C., Tempel, H. & Härdtle, W. Structural pattern of a near-natural beech forest (Fagus sylvatica) (Serrahn, North-east Germany). For. Ecol. Manag. 212, 253–263 (2005).Article 

Google Scholar 
Kunstler, G., Curt, T. & Lepart, J. Spatial pattern of beech (Fagus sylvatica L.) and oak (Quercus pubescens Mill.) seedlings in natural pine (Pinus sylvestris L.) woodlands. Eur. J. For. Res. 123(4), 331–337 (2004).Article 

Google Scholar 
Mosandl, R. & Kleinert, A. Development of oaks (Quercus petraea (Matt.) Liebl.) emerged from bird-dispersed seeds under old-growth pine (Pinus sylvestris L.) stands. For. Ecol. Manag. 106, 35–44 (1998).Article 

Google Scholar 
Hosseini, A., Jafari, M. R. & Askari, S. Investigation and recognition of ecological characteristics of sites of Persian oak and pistachio old trees in forests of Ilam province. Wood Sci. Technol. 26(4), 113–128 (2019).
Google Scholar 
Ghalandarayeshi, S., Nord-Larsen, T., Johannsen, V. K. & Larsen, J. B. Spatial patterns of tree species in Suserup Skov—A semi-natural forest in Denmark. For. Ecol. Manag. 406, 391–401 (2017).Article 

Google Scholar 
Petritan, I. C., Marzano, R., Petritan, A. M. & Lingua, E. Overstory succession in a mixed Quercus petraea-Fagus sylvatica old growth forest revealed through the spatial pattern of competition and mortality. For. Ecol. Manag. 326, 9–17 (2014).Article 

Google Scholar 
Watt, A. S. On the ecology of British Beech woods with special reference to their regeneration: Part II, sections II and III. The development and structure of beech communities on the Sussex downs. J. Ecol. 13, 27–73 (1925).Article 

Google Scholar 
Wiegand, T., Gunatilleke, S., Gunatilleke, N. & Okuda, T. Analyzing the spatial structure of a Sri Lankan tree species with multiple scales of clustering. Ecology 88, 3088–3102 (2007).PubMed 
Article 

Google Scholar 
Moradi, M., Marvie Mohadjer, M. R., Sefidi, K., Zobiri, M. & Omidi, A. Over matured beech trees (Fagus orientalis Lipsky.) component of close to nature forestry in northern Iran. J. For. Res. 23(2), 289–294 (2012).Article 

Google Scholar 
Lan, G. Y. et al. Spatial dispersion patterns of trees in a tropical rainforest in Xishuangbanna, southwest China. Ecol. Res. 24, 1117–1124 (2009).ADS 
Article 

Google Scholar 
Lan, G., Hu, Y., Cao, M. & Zhu, H. Topography related spatial distribution of dominant tree species in a tropical seasonal rain forest in China. For. Ecol. Manag. 262(8), 1507–1513 (2011).Article 

Google Scholar 
Menendez, I., Moreno, G., Fernando Gallardo Lancho, J. & Saavedra, J. Soil solution composition in forest soils of sierra de gata mountains, Central-Western Spain: Relationship with soil water content. Arid Land Res. Manag. 9(4), 495–502 (1995).
Google Scholar 
Kopecký, M., Macek, M. & Wild, J. Topographic Wetness Index calculation guidelines based on measured soil moisture and plant species composition. Sci. Total Environ. 757, 143785 (2021).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Delfan Abazari, B., Sagheb-Talebi, K. & Namiranian, M. Development stages and dynamic of undisturbed Oriental beech (Fagus orientalis Lipsky) stands in Kelardasht region (Iran). Iran. J. For. Poplar Res. 12, 307–326 (2004) ((in Persian)).
Google Scholar 
Sagheb-Talebi K., Delfan Abazari B. & Namiranian M. Description of decay stage in a natural Oriental beech (Fagus orientalis Lipsky) forest in Iran, preliminary results. In Natural Forests in the Temperate Zone of Europe – Values and Utilization (eds. Commarmot, B. & Hamor, F.D.), Proceedings of conference in Mukachevo, Oct 13–17, 130–134 (2003).Christensen, M., Emborg, J. & Nielsen, A. B. The forest cycle of Suserup Skov: Revisited and revised. Ecol. Bull. 52, 33–42 (2007).
Google Scholar 
Dobrowolska, D. et al. A review of European ash (Fraxinus excelsior L.): Implications for silviculture. Forestry 84, 133–148 (2011).Article 

Google Scholar 
Akhani, H., Djamali, M., Ghorbanalizadeh, A. & Ramezani, E. Plant biodiversity of Hyrcanian relict forests, N Iran: An overview of the flora, vegetation, palaeoecology and conservation. Pak. J. Bot. 42(1), 231–258 (2010).
Google Scholar 
Pourmajidian, M. R. et al. Effect of shelterwood cutting method on forest regeneration and stand structure in a Hyrcanian forest ecosystem. J. For. Res. 21, 265–272 (2010).Article 

Google Scholar 
Szwagrzyk, J. & Szewczyk, J. Tree mortality and effects of release from competition in an old-growth Fagus-Abies-Picea stand. J. Veg. Sci. 12, 621–626 (2001).Article 

Google Scholar 
Janík, D. et al. Tree spatial patterns of Fagus sylvatica expansion over 37 years. For. Ecol. Manag. 375, 134–145 (2016).Article 

Google Scholar 
Amiri, M. Dynamics of Structural Characteristics of a Natural Unlogged Fagus orientalis Lipsky Stand during a 5-year’s Period in Shast-Kalate Forest, Gorgan, Iran, Ph.D. Dissertation, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan (2013) (in Persian).Soofi, M. Effects of anthropogenic pressure on large mammal species in the Hyrcanian forest, Iran: Effects of poaching, logging and livestock grazing on large mammals (Doctoral dissertation, Dissertation, Göttingen, Georg-August Universität, 2018). More

Ellison, W. T., Southall, B. L., Clark, C. W. & Frankel, A. S. A new context-based approach to assess marine mammal behavioral responses to anthropogenic sounds. Conserv. Biol. 26, 21–28 (2012).CAS 
PubMed 
Article 

Google Scholar 
Williams, R., Clark, C. W., Ponirakis, D. & Ashe, E. Acoustic quality of critical habitats for three threatened whale populations. Anim. Conserv. 17, 174–185 (2014).Article 

Google Scholar 
Halliday, W. D., Pine, M. K. & Insley, S. J. Underwater noise and arctic marine mammals: Review and policy recommendations. Environ. Rev. 28, 438–448 (2020).Article 

Google Scholar 
Kvadsheim, P. H. et al. Impact of Anthropogenic Noise on the Marine Environment: Status of Knowledge and Management (Springer, 2020).
Google Scholar 
Weilgart, L. S. & Whitehead, H. Distinctive vocalizations from mature male sperm whales (Physeter macrocephalus). Can. J. Zool. 66, 1931–1937 (1988).Article 

Google Scholar 
Simon, M., Stafford, K. M., Beedholm, K., Lee, C. M. & Madsen, P. T. Singing behavior of fin whales in the Davis Strait with implications for mating, migration and foraging. J. Acoust. Soc. Am. 128, 3200 (2010).ADS 
PubMed 
Article 

Google Scholar 
Alves, D., Amorim, M. C. P. & Fonseca, P. J. Assessing acoustic communication active space in the Lusitanian toadfish. J. Exp. Biol. 219, 1122–1129 (2016).PubMed 

Google Scholar 
Linnenschmidt, M., Teilmann, J., Akamatsu, T., Dietz, R. & Miller, L. A. Biosonar, dive, and foraging activity of satellite tracked harbor porpoises (Phocoena phocoena). Mar. Mamm. Sci. 29, E77–E97 (2013).Article 

Google Scholar 
Giorli, G. & Goetz, K. T. Foraging activity of sperm whales (Physeter macrocephalus) off the east coast of New Zealand. Sci. Rep. 9, 12182 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Baumgartner, M. F. & Fratantoni, D. M. Diel periodicity in both sei whale vocalization rates and the vertical migration of their copepod prey observed from ocean gliders. Limnol. Oceanogr. 53, 2197–2209 (2008).ADS 
Article 

Google Scholar 
Urazghildiiev, I. R. & Van Parijs, S. M. Automatic grunt detector and recognizer for Atlantic cod (Gadus morhua). J. Acoust. Soc. Am. 139, 2532–2540 (2016).ADS 
PubMed 
Article 

Google Scholar 
Ladich, F. Ecology of sound communication in fishes. Fish Fish. 20, 552–563 (2019).Article 

Google Scholar 
Radford, C. A., Stanley, J. A., Simpson, S. D. & Jeffs, A. G. Juvenile coral reef fish use sound to locate habitats. Coral Reefs 30, 295–305 (2011).ADS 
Article 

Google Scholar 
Pierpoint, C. Harbour porpoise (Phocoena phocoena) foraging strategy at a high energy, near-shore site in south-west Wales, UK. J. Mar. Biol. Assoc. UK 88, 1167–1173 (2008).Article 

Google Scholar 
Pijanowski, B. C. et al. Soundscape ecology: The science of sound in the landscape. Bioscience 61, 203–216 (2011).Article 

Google Scholar 
Stanley, J. A., Radford, C. A. & Jeffs, A. G. Location, location, location: Finding a suitable home among the noise. Proc. R. Soc. B Biol. Sci. 279, 3622–3631 (2012).Article 

Google Scholar 
Buscaino, G. et al. Temporal patterns in the soundscape of the shallow waters of a Mediterranean marine protected area. Sci. Rep. 6, 1–13 (2016).Article 
CAS 

Google Scholar 
Gasc, A., Francomano, D., Dunning, J. B. & Pijanowski, B. C. Future directions for soundscape ecology: The importance of ornithological contributions. Auk 134, 215–228 (2017).Article 

Google Scholar 
Putland, R. L., Constantine, R. & Radford, C. A. Exploring spatial and temporal trends in the soundscape of an ecologically significant embayment. Sci. Rep. 7, 5713 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pieretti, N., LoMartire, M., Farina, A. & Danovaro, R. Marine soundscape as an additional biodiversity monitoring tool: A case study from the Adriatic Sea (Mediterranean Sea). Ecol. Indic. 83, 13–20 (2017).Article 

Google Scholar 
Gillespie, D., Palmer, L., Macaulay, J., Sparling, C. & Hastie, G. Passive acoustic methods for tracking the 3D movements of small cetaceans around marine structures. PLoS ONE 15, e0229058 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Van Parijs, S. et al. Management and research applications of real-time and archival passive acoustic sensors over varying temporal and spatial scales. Mar. Ecol. Prog. Ser. 395, 21–36 (2009).ADS 
Article 

Google Scholar 
Warren, V. E., McPherson, C., Giorli, G., Goetz, K. T. & Radford, C. A. Marine soundscape variation reveals insights into baleen whales and their environment: a case study in central New Zealand. R. Soc. Open Sci. 8, 201503 (2021).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ahonen, H. et al. The underwater soundscape in western Fram Strait: Breeding ground of Spitsbergen’s endangered bowhead whales. Mar. Pollut. Bull. 123, 97–112 (2017).CAS 
PubMed 
Article 

Google Scholar 
Hildebrand, J. A. Anthropogenic and natural sources of ambient noise in the ocean. Mar. Ecol. Prog. Ser. 395, 5–20 (2009).ADS 
Article 

Google Scholar 
Ross, D. Ship sources of ambient noise. IEEE J. Ocean. Eng. 30, 257–261 (2005).ADS 
Article 

Google Scholar 
Popper, A. N. & Hawkins, A. The Effects of Noise on Aquatic Life Vol. 730 (Springer, 2012).Book 

Google Scholar 
Hubert, J. et al. Effects of broadband sound exposure on the interaction between foraging crab and shrimp: A field study. Environ. Pollut. 243, 1923–1929 (2018).CAS 
PubMed 
Article 

Google Scholar 
Weilgart, L. The Impact of Ocean Noise Pollution on Fish and Invertebrates (Springer, 2018).
Google Scholar 
Kvadsheim, H., Sivle, L. D., Hansen, R. R. & Karlsen, H. E. Effekter av Menneskeskapt støy på Havmiljø Rapport til Miljødirektoratet om Kunnskapsstatus FFI-RAPPORT. (2017).Parks, S. E., Johnson, M., Nowacek, D. & Tyack, P. L. Individual right whales call louder in increased environmental noise. Biol. Lett. 7, 33–35 (2011).PubMed 
Article 

Google Scholar 
Meh, F. et al. Humpback whales Megaptera novaeangliae alter calling behavior in response to natural sounds and vessel noise. Mar. Ecol. Prog. Ser. 607, 251–268 (2018).Article 

Google Scholar 
Leroy, E. C., Royer, J.-Y., Bonnel, J. & Samaran, F. Long-term and seasonal changes of large whale call frequency in the Southern Indian ocean. J. Geophys. Res. Ocean. 123, 8568–8580 (2018).ADS 
Article 

Google Scholar 
Parks, S. E., Clark, C. W. & Tyack, P. L. Short- and long-term changes in right whale calling behavior: The potential effects of noise on acoustic communication. J. Acoust. Soc. Am. 122, 3725–3731 (2007).ADS 
PubMed 
Article 

Google Scholar 
Clark, C. et al. Acoustic masking in marine ecosystems: intuitions, analysis, and implication. Mar. Ecol. Prog. Ser. 395, 201–222 (2009).ADS 
Article 

Google Scholar 
PAME. Underwater Noise in the Arctic: A State of Knowledge Report (PAME, 2019).
Google Scholar 
Beszczynska-Möller, A., Woodgate, R., Lee, C., Melling, H. & Karcher, M. A synthesis of exchanges through the main oceanic gateways to the Arctic Ocean. Oceanography 24, 82–99 (2011).Article 

Google Scholar 
Ramm, T. Hungry During Migration? Humpback Whale Movement from the Barents Sea to a Feeding Stopover in Northern Norway Revealed by Photo-ID Analysis. (MSc thesis. UiT The Arctic University of Norway, 2020).Broms, F. et al. Recent research on the migratory destinations of humpback whales (Megaptera novaeangliae) from a mid-winter feeding stop-over area in Northern Norway. in Recent research on the migratory destinations of humpback whales (Megaptera novaeangliae) from a mid-winter feeding stop-over area in Northern Norway (ed. Wenzel, F. W.) (European Cetacean Society Special Publication Series, 2015).Aniceto, A. S. et al. Arctic marine data collection using oceanic gliders: Providing ecological context to cetacean vocalizations. Front. Mar. Sci. 7, 547 (2020).Article 

Google Scholar 
Jourdain, E. & Vongraven, D. Humpback whale (Megaptera novaeangliae) and killer whale (Orcinus orca) feeding aggregations for foraging on herring (Clupea harengus) in Northern Norway. Mamm. Biol. 86, 27–32 (2017).Article 

Google Scholar 
Christiansen, J. S., Mecklenburg, C. W. & Karamushko, O. V. Arctic marine fishes and their fisheries in light of global change. Glob. Chang. Biol. 20, 352–359 (2014).ADS 
PubMed 
Article 

Google Scholar 
Rødland, E. S. & Bjørge, A. Residency and abundance of sperm whales (Physeter macrocephalus) in the Bleik Canyon, Norway. Mar. Biol. Res. 11, 974–982 (2015).Article 

Google Scholar 
Nøttestad, L. et al. Prey selection of offshore killer whales Orcinus orca in the Northeast Atlantic in late summer: spatial associations with mackerel. Mar. Ecol. Prog. Ser. 499, 275–283 (2014).ADS 
Article 

Google Scholar 
Bjørge, A., Aarefjord, H., Kaarstad, S., Kleivane, L. & Øien, N. Harbour porpoise (Phocoena phocoena) in Norwegian waters (Springer, 1991).
Google Scholar 
Gjøseter, H. et al. Fisken og Havet. https://doi.org/10.1111/maec.12075 (2010).Article 

Google Scholar 
ICES. ICES Report on Ocean Climate 2009 No.304. (2010).ICES. Report of the Working Group on Widely Distributed Stocks (WGWIDE). (2010).Rey, F. Phytoplankton: The grass of the sea. In The Norwegian Sea Ecosystem (ed. Skjoldal, H. R.) 97–136 (Academic Press, 2004).
Google Scholar 
Huse, G. et al. Effects of interactions between fish populations on ecosystem dynamics in the Norwegian Sea : Results of the INFERNO project. Mar. Biol. Res. 8, 415–419 (2012).Article 

Google Scholar 
Godø, O. R., Johnsen, S. & Torkelsen, T. The LoVe ocean observatory is in operation. Mar. Technol. Soc. J. 48, 24–30 (2014).Article 

Google Scholar 
Cooke, J. G. Balaenoptera physalus. The IUCN Red List of Threatened Species: e.T2478A50349982 (2018).Leonard, D. & Øien, N. Estimated abundances of Cetacean species in the Northeast Atlantic from Norwegian Shipboard Surveys Conducted in 2014–2018. NAMMCO Sci. Publ. 11, 4694 (2020).
Google Scholar 
Øygard, S. H. Simulations of Acoustic Transmission Loss of Fin Whale Calls Reaching the LoVe Ocean Observatory. (MSc thesis. University of Bergen, 2018).Steiner, L. et al. A link between male sperm whales, Physeter macrocephalus, of the Azores and Norway. J. Mar. Biol. Assoc. UK 92, 1751–1756 (2012).Article 

Google Scholar 
Olafsen, T., Winther, U., Olsen, Y. & Skjermo, J. Verdiskaping Basert på Produktive hav i 2050 1–76 (Springer, 2012).
Google Scholar 
Wenz, G. M. Acoustic ambient noise in the ocean: Spectra and sources. J. Acoust. Soc. Am. 34, 1936–1956 (1962).ADS 
Article 

Google Scholar 
Klinck, H. et al. Seasonal presence of cetaceans and ambient noise levels in polar waters of the North Atlantic. J. Acoust. Soc. Am. 132, 176–181 (2012).Article 

Google Scholar 
Burnham, R. E., Duffus, D. A. & Mouy, X. The presence of large whale species in Clayoquot Sound and its offshore waters. Cont. Shelf Res. 177, 15–23 (2019).ADS 
Article 

Google Scholar 
Romagosa, M. et al. Baleen whale acoustic presence and behaviour at a Mid-Atlantic migratory habitat, the Azores Archipelago. Sci. Rep. 10, 61489 (2020).
Google Scholar 
Tervo, O. Acoustic Behaviour of Bowhead Whales Balaena mysticetus in Disko Bay, Western Greenland. PhD thesis. (2011).Magnúsdóttir, E. E. & Lim, R. Subarctic singers: Humpback whale (Megaptera novaeangliae) song structure and progression from an Icelandic feeding ground during winter. PLoS ONE 14, e0210057 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Samaran, F. et al. Seasonal and geographic variation of southern blue whale subspecies in the Indian Ocean. PLoS ONE 8, e70 (2013).Article 

Google Scholar 
Norris, T. F., Dunleavy, K. J., Yack, T. M. & Ferguson, E. L. Estimation of minke whale abundance from an acoustic line transect survey of the Mariana Islands. Mar. Mammal Sci. 33, 574 (2017).Article 

Google Scholar 
Marques, T. A. et al. Estimating animal population density using passive acoustics. Biol. Rev. Camb. Philos. Soc. 88, 287–309 (2013).PubMed 
Article 

Google Scholar 
Dunlop, R. A. The effects of vessel noise on the communication network of humpback whales. R. Soc. Open Sci. 6, 190967 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Christensen, I., Haug, T. & Øien, N. A review of feeding and reproduction in large baleen whales (Mysticeti) and sperm whales Physeter macrocephalus in Norwegian and adjacent waters. ICES J. Mar. Sci. 49, 341–355 (1992).Article 

Google Scholar 
Aniceto, A. S. et al. Monitoring marine mammals using unmanned aerial vehicles: quantifying detection certainty. Ecosphere 9, e02122 (2018).Article 

Google Scholar 
Pedersen, G., Storheim, E., Sivle, L. D., Godø, O. R. & Ødegaard, L. A. Concurrent passive and active acoustic observations of high-latitude shallow foraging sperm whales (Physeter macrocephalus) and mesopelagic prey layer. J. Acoust. Soc. Am. 141, 1–10 (2017).Article 

Google Scholar 
Vogel, E. F. The influence of herring (Clupea harengus) biomass and distribution on killer whale (Orcinus orca) movements on the Norwegian shelf (UiT The Arctic University of Norway, 2020).
Google Scholar 
Williams, R. et al. Impacts of anthropogenic noise on marine life: Publication patterns, new discoveries, and future directions in research and management. Ocean Coast. Manag. 115, 17–24 (2015).Article 

Google Scholar 
Garibbo, S. et al. Low-frequency ocean acoustics: Measurements from the Lofoten-Vesterålen Ocean Observatory, Norway. (2020). https://doi.org/10.1121/2.0001324.Dekeling, R. P. A. et al. Monitoring Guidance for Underwater Noise in European Seas, Part I: Executive Summary (Springer, 2014).
Google Scholar 
Erbe, C. International regulation of underwater noise. Acoust. Aust. 41, 1–10 (2013).
Google Scholar 
Halliday, W. D., Insley, S. J., Hilliard, R. C., de Jong, T. & Pine, M. K. Potential impacts of shipping noise on marine mammals in the western Canadian Arctic. Mar. Pollut. Bull. 123, 73–82 (2017).CAS 
PubMed 
Article 

Google Scholar 
Halliday, W. D. et al. Underwater sound levels in the Canadian Arctic, 2014–2019. Mar. Pollut. Bull. 168, 112437 (2021).CAS 
PubMed 
Article 

Google Scholar 
Ødegaard, L., Pedersen, G. & Johnsen, E. Underwater Noise From Wind At the High North Love Ocean Observatory. UACE 2019 Conf. Proc. 359–366 (2019).Zhang, G., Forland, T. N., Johnsen, E., Pedersen, G. & Dong, H. Measurements of underwater noise radiated by commercial ships at a cabled ocean observatory. Mar. Pollut. Bull. 153, 110948 (2020).CAS 
PubMed 
Article 

Google Scholar 
Maystrenko, Y. P., Olesen, O., Gernigon, L. & Gradmann, S. Deep structure of the Lofoten–Vesterålen segment of the Mid-Norwegian continental margin and adjacent areas derived from 3-D density modeling. J. Geophys. Res. Solid Earth 122, 1402–1433 (2017).ADS 
Article 

Google Scholar 
Gillespie, D. et al. PAMGUARD: Semiautomated, open source software for real-time acoustic detection and localization of cetaceans. J. Acoust. Soc. Am. 125, 2547–2547 (2009).ADS 
Article 

Google Scholar 
Hollander, M. & Wolfe, D. A. Nonparametric Statistical Methods (Wiley, 1973).MATH 

Google Scholar 
Vogel, E. F. et al. Killer whale movements on the Norwegian shelf are associated with herring density. Mar. Ecol. Prog. Ser. 665, 217–231 (2021).ADS 
Article 

Google Scholar 
Garcia, H. A. et al. Temporal-spatial, spectral, and source level distributions of fin whale vocalizations in the Norwegian Sea observed with a coherent hydrophone array. ICES J. Mar. Sci. 76, 268–283 (2019).Article 

Google Scholar 
Davis, G. E. et al. Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data. Glob. Chang. Biol. 26, 4812–4840 (2020).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Risch, D. et al. Minke whale acoustic behavior and multi-year seasonal and diel vocalization patterns in Massachusetts Bay, USA. Mar. Ecol. Prog. Ser. 489, 279–295 (2013).ADS 
Article 

Google Scholar 
Le Tixerant, M., Le Guyader, D., Gourmelon, F. & Queffelec, B. How can Automatic Identification System (AIS) data be used for maritime spatial planning?. Ocean Coast. Manag. 166, 18–30 (2018).Article 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).Sumner, M. D. The Tag Location Problem. 133 (2011).Sumner, M. D., Wotherspoon, S. J. & Hindell, M. A. Bayesian estimation of animal movement from archival and satellite tags. PLoS ONE 4, e7324 (2009).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Halliday, W. D. et al. The coastal Arctic marine soundscape near Ulukhaktok, Northwest Territories, Canada. Polar Biol. 43, 623–636 (2020).Article 

Google Scholar 
Ezzet, F. & Pinheiro, J. Linear, generalized linear, and nonlinear mixed effects models. Pharm. Sci. Quant. Pharmacol. 1, 103–135. https://doi.org/10.1002/9780470087978.ch4 (2006).Article 

Google Scholar 
Mazerolle, M. J. AICcmodavg: Model Selection and Multimodel Inference Based on (Q)AIC(c). (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
Pante, E. & Simon-Bouhet, B. marmap: A package for importing, plotting and analyzing bathymetric and topographic data in R. PLoS ONE 8, e73051 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wessel, P. & Walter, H. F. S. A global self-consistent, hierarchical, high-resolution shoreline database. J. Geophys. Res. 101, 8741–8743 (1996).ADS 
Article 

Google Scholar 
Sueur, J., Aubin, T. & Simonis, C. Equipment review: Seewave, a free modular tool for sound analysis and synthesis. Bioacoustics 18, 213–226 (2008).Article 

Google Scholar 
The Mathworks Inc. MATLAB (R2019a). (MathWorks Inc., 2019).Merchant, N. D. et al. Measuring acoustic habitats. Methods Ecol. Evol. 6, 257–265 (2015).PubMed 
PubMed Central 
Article 

Google Scholar  More

Diomedenema dinarctos, a parasitic worm that infests penguins, is named after the Greek deinos, meaning terrible, and arktos, or bear, because of its resemblance to a menacing teddy bear.Credit: Bronwen Presswell and Jerusha Bennett

What scientists choose to name parasitic worms could say more about the researchers than the organism they are studying.A study1 examining the names of nearly 3,000 species of parasitic worm discovered in the past 20 years reveals a markedly higher proportion named after male scientists than after female scientists — and a growing appetite for immortalizing friends and family members in scientific names.The analysis uncovers ongoing biases in taxonomy — the classification of organisms — and could be used as a jumping-off point for rethinking how scientists name species, says study co-author Robert Poulin, an ecological parasitologist at the University of Otago in Dunedin, New Zealand.“When you name something, it is now named forever. I think it’s worth giving some thought to what names we choose,” he says. The research was published on 11 May in Proceedings of the Royal Society B.As the worm turnsSpecies names often describe how an organism looks or where it was found. But since the nineteenth century, they have also been used to immortalize scientists. The parasite that causes the intestinal disease giardiasis, for instance, was named after French zoologist Alfred Giard.Wondering how naming practices had changed, Poulin and his colleagues combed through papers published between 2000 and 2020 that describe roughly 2,900 new species of parasitic worm. The team found that well over 1,500 species were named after their host organism, where they were found or a prominent feature of their anatomy.Many others were named after people, ranging from technical assistants to prominent politicians (Baracktrema obamai, a species found in Malaysian freshwater turtles, was named after former US president Barack Obama). But just 19% of the 596 species named after eminent scientists were named after women, a percentage that essentially didn’t budge over the decades (see ‘Parasite name game’).

Source: Ref. 1

This could be because of a historical dearth of female figures in the field, says Janine Caira, a parasite taxonomist at the University of Connecticut in Storrs. But another possibility is that the work of past female scientists often goes unrecognized, says Tanapan Sukee, a parasitologist at the University of Melbourne in Australia.Sukee has named two species of parasitic worm after now-deceased Australian biologist Patricia Mawson, who was a key player in the characterization of marsupial parasites. For most of her career, Mawson worked part-time as a technician, and she was often designated second author on papers describing species she had discovered, Sukee says. Similar situations could explain why so few parasites are named after women.Poulin and his colleagues also noticed an upward trend in the number of parasites named after friends and family members of the scientists who formally described them. Some researchers even name species after pets: Rhinebothrium corbatai is a freshwater stingray parasite named after the first author’s Welsh terrier, Corbata.Poulin says this should be discouraged. Species are almost never named after the person who described them, and Poulin argues that names honouring parents, children or spouses could be seen as a way to get around this convention.And besides, “I don’t have any friends or family who want a parasite named after them!” says Sukee. More