Philippa Kaur

Data collectionThe acoustic recordings were collected during 2017 off the Changhua coast (24° 4.283 N/120° 19.102 E) (Fig. 5) by deploying a passive acoustic monitoring (PAM) device from Wildlife Acoustics, which was an SM3M recorder moored at a depth of 18–20 m. The hydrophone recorded continuously with a sampling frequency of 48 kHz and a sensitivity of −164.2 dB re:1 v/µPa. The acoustic files were recorded in the.WAV format with a duration of 60 minutes. The hydrophone setup prior to deployment is shown in Fig. 6. Table 2 contains the details for the monitoring period with the corresponding season and the number of hours of recordings each season used in this study. Studies have shown that the presence of seasonal chorusing at this monitoring site in the frequency range of 500–2500 Hz is caused by two types of chorusing15,38, with chorusing starting in early spring, reaching a peak in summer, and starting to decline late autumn, and silencing in winter6. Previous studies6,15,38 at this monitoring site have derived the details of two types of fish calls responsible for chorusing (Type 1 and Type 2); Supplementary Fig. 1 shows the spectrogram, waveform, and power spectrum density of the individual calls. Supplementary Table 1 tabulated are the acoustic features of the two call types. The monitoring region, Changhua, lies in the Eastern Taiwan Strait (ETS). The ETS is ~350 km in length and ~180 km wide64. The ETS experiences diverse oceanographic and climatic variations influenced by monsoons in summer and winter65 and extreme events caused by tropical storms, typhoons in summer, and wind/cold bursts occurring in winter66,67,68.Fig. 5: Study area located off the Taiwan Strait.Map of the Changhua coast located in Taiwan Strait, Taiwan depicting the deployed passive acoustic monitoring recorder at site A1. The map was produced in Matlab 9.11 (The Mathworks, Natick, MA; http://www.mathworks.com/) using mapping toolbox function geobasemap(). Full global basemap composed of high-resolution satellite imagery hosted by Esri (https://www.esri.com/).Full size imageFig. 6: Setup of the SM3M submersible recorder.SM3M recorder fastened to the steel frame (length and breadth = 1.22 m, height = 0.52 m) with plastic cable zip ties prior to deployment.Full size imageTable 2 Passive acoustic monitoring device specifications and monitoring duration during different seasons.Full size tableAcoustic data analysisThe acoustic data were analyzed using the PAMGuide toolbox in Matlab60. The seasonal spectrograms were computed with an FFT size of 1024 points and a 1 s time segment averaged to a 60 s resolution. The sound pressure levels (SPL) were computed in the frequency band of 500–3500 Hz and programmed to provide a single value every hour, thus resulting in 984, 1344, and 1440 data points in spring, summer, and winter, respectively (Supplementary Data 1).Determining the regularity and complexity with the complexity-entropy planeThe complexity-entropy plane was utilized in this study to quantify the structural statistical complexity and the regularity in the hourly acoustical and seasonal SPL time series data. The C-H plane is a 2D plane representation of the permutation entropy on the horizontal axis that quantifies the regularity, and the vertical axis is represented by the statistical complexity quantifying the correlation structure in the temporal series.For a given time series ({{x(t)}}_{t=1}^{N}), the N’ ≡ N − (m − 1) the values of the vectors for the length m  > 1 are ranked as$${X}_{s}=left({x}_{s-(m-1)},{x}_{s-(m-2)},ldots ,{x}_{s}right),s=1,ldots ,,{N}^{{prime} }$$
(1)
Within each vector, the values are reordered in the ascending order of their amplitude, yielding the set of ordering symbols (patterns) ({r}_{0},{r}_{1},ldots ,{r}_{m-1}) such that$${x}_{s-{r}_{0}}le {x}_{s-{r}_{1}}le ..,..le {x}_{s-{r}_{(m-1)}}$$
(2)
This symbolization scheme was introduced by Bandt and Pompe69. The scheme performs the local ordering of a time series to construct a probability mass function (PMF) of the ordinal patterns of the vector. The corresponding vectors (pi ={r}_{0},{r}_{1},ldots ,{r}_{(m-1)}) may presume any of the m! possible permutations of the set ({{{{{mathrm{0,1}}}}},ldots ,m-1}) and symbolically represent the original vector. For instance, for a given time series {9, 4, 5, 6, 1,…} with length m = 3, provides 3! different order patterns with six mutually exclusive permutation symbols are considered. The first three-dimensional vector is (9, 4, 5), following the Eq. (1), this vector corresponds to ((,{x}_{s-2},{x}_{s-1},{x}_{s})). According to Eq. (2), it yields ({x}_{s-1}le {x}_{s}le {x}_{s-2}). Then, the ordinal pattern satisfying the Eq. (2) will be (1, 0, 2). The second 3-dimensional vector is (4, 5, 6), and (2, 1, 0) will be its associated permutation, and so on.The permutation entropy (H) of order m ≥ 2 is defined as the Shannon entropy of the Brandt-Pompe probability distribution p(π)69$$Hleft(mright)=,-{mathop{sum}limits _{{pi }}}pleft(pi right){{{log }}}_{2}p(pi )$$
(3)
where ({pi }) represents the summation over all possible m! permutations of order m, (p(pi )) is the relative frequency of each permutation (pi), and the binary logarithm (base of 2) is evaluated to quantify the entropy in bits. H(m) attains the maximum ({{log }}m!) for (p(pi )=1/m!). Then the normalized Shannon entropy is given by$$0le H(m)/{{{{{rm{ln}}}}}},m!le 1$$
(4)
where the lower bound H = 0 corresponds to more predictable signals with fewer fluctuations, an either strictly increasing or decreasing series (with a single permutation), and the upper bound H = 1 corresponds to an unpredictable random series for which all the m! possible permutations are equiprobable. Thus, H quantifies the degree of disorder inherent in the time series. The choice of the pattern length m is essential for calculating the appropriate probability distribution, particularly for m, which determines the number of accessible states given by m!70,71. As a rule of thumb, the length T of the time series must satisfy the condition T (gg) m! in order to obtain reliable statistics, and for practical purposes, Bandt and Pompe suggested choosing m = 3,…,7 69.The statistical complexity measure is defined with the product form as a function of the Bandt and Pompe probability distribution P associated with the time series. (Cleft[Pright]) is represented as33$$Cleft[Pright]=frac{J[P,U]}{{J}_{{max }}}{H}_{s}[P]$$
(5)
where ({H}_{s}left[Pright]=Hleft[Pright]/{{log }}m!) is the normalized permutation entropy. (J[P,U]) is the Jensen divergence$$Jleft[P,Uright]=left{Hleft[frac{P+U}{2}right]-frac{H[P]}{2}-frac{H[U]}{2}right}$$
(6)
which quantifies the difference between the uniform distributions U and P, and ({J}_{{max }})is the maximum possible value of (Jleft[P,Uright]) that is obtained from one of the components of P = 1, with all the other components being zero:$$Jleft[P,Uright]=-frac{1}{2}left[frac{m!+1}{m!}{{log }}left(m!+1right)-2{{log }}left(2m!right)+{{log }}(m!)right]$$
(7)
For each value of the normalized permutation entropy (0le {H}_{s}[P]le 1) there is a corresponding range of possible statistical complexity (Cleft[Pright]) values. Thus, the upper (({C}_{{max }})) and lower ((C_{{min }})) complexity bounds in the C-H plane are formed. The periodic sequences such as sine and series with increasing and decreasing (with ({H}_{s}[P]=0)) and completely random series such as white noise (for which (Jleft[P,Uright]=0) and ({H}_{s}[P]=1)) will have zero complexity. Furthermore, for each given value of the (0le {H}_{s}[P]le 1), there exists a range of possible values of the statistical complexity, ({C}_{{min }}le C[P]le {C}_{{max }}). The procedure for evaluating the complexity bounds ({C}_{{min }}) and ({C}_{{max }}) is given in Martin et al.72. We utilized the R package ‘statcomp’73 to evaluate the statistical complexity (C) and the permutation entropy (H) using the command global-complexity() for the order m = 6, and the command limit_curves(m, fun = ‘min/max’) was utilized to evaluate the complexity boundaries ({C}_{{min }}) and ({C}_{{max }}). In this study, we constructed two C-H planes: (1) C and H was computed for each hourly acoustic file during different seasons. The resulting lengths of C and H during spring, summer, and autumn-winter are similar to the number of hours in the particular season (Table 2). (2) C and H was computed every 4–5 days for the seasonal SPL. The resulting length of C and H obtained during spring was 9 points (each value of C and H for every 109 h), and in summer and autumn-winter was 12 points (each value of C and H for every 112 and 120 h).Determining predictability and dynamics (linear/nonlinear) using EDMIn this study, we utilized EDM to quantify the predictability (forecasting) and distinguish between the linear stochastic and nonlinear dynamics in the seasonal soundscape SPL. EDM involves phase-space reconstruction via delay coordinate embeddings to make forecasts and to determine the ‘predictability portrait’ of time series data40. The first step in EDM is to determine the optimal embedding dimension (E), and this is obtained using a method based on simplex projection41. The simplex projection is carried out by dividing the dataset into two equal parts, of which the first part is called the library and the other part is called the target. The library set is used to build a series of non-parametric models (known as predictors) for the one step ahead predictions for the E varying between 1 and 10. Then the model’s accuracies are determined when the model is applied to the target dataset and the prediction skill (⍴) for the actual and predicted datasets is measured. The embedding dimension with the highest predictive skill is the optimal E.For the appropriate optimal E chosen, the predictability profile of the time series data is obtained by evaluating ⍴ at Tp = 1, 2, 3, … steps ahead. The flat prediction profile of the ⍴–Tp curve indicates that the time series is purely random (low ⍴) or regularly oscillating (high ⍴). In contrast, a decreasing ⍴ as Tp increases may reject the possibility of an underlying uncorrelated stochastic process and indicate the presence of low-dimensional deterministic dynamics. However, the concern with the predictability profile is that it may exhibit predictability even if time series are purely stochastic (such as autocorrelated red noise). Hence, a nonlinear test can be performed by using S-maps (sequential locally weighted global linear maps) to distinguish between linear stochastic and nonlinear dynamics in the time series dataset by fitting a local linear map. S-maps similar to simplex projects provide the forecasts in phase-space by quantifying the degree to which points are weighted when fitting the local linear map, which is given by the nonlinear localization parameter θ. When θ = 0, the entire library set will exhibit equal weights regardless of the target prediction, which mathematically resembles the model of a linear autoregressive process. In contrast, if θ  > 0, the forecasts of the library provided by the S-map depend on the local state of the target prediction, thus producing large weights, and the unique local fittings can vary in phase-space to incorporate nonlinear behavior. Consequently, if the (⍴–θ) dynamics profile shows the highest ⍴ at θ = 0 that is reduced as θ increases, it represents linear stochastic dynamics. If the ⍴ achieves the highest value at θ  > 0, then the dynamics are represented by nonlinear dynamics.In this study, the R package “rEDM”74 was used to evaluate the optimal E, prediction profile (⍴–Tp), and dynamics profile (⍴–θ) for the seasonal SPL dataset. While evaluating these entities, the data points are equally into two parts, and sequentially the first half is chosen as the library set and the other as the target set. The length of the library and the target set for spring, summer, and autumn-winter are 492, 672, and 720. The command EmbedDimension() was used to determine the forecast skill for the E ranging from 1 to 10 and the optimal E with the highest forecast skill (Supplementary Fig. 2) was chosen. In this study, we found that for all seasons, the optimal E was 2. The (⍴–Tp) was evaluated for Tp varying between 1 and 100 using the command PredictInterval() and the (⍴–θ) was evaluated using the command PredictNonlinear() for θ = 0, 0.0001, 0.0003, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 0.5,0.75, 1.0, 1.5, 2, and 3 to 20.StatisticsThe nonparametric Kruskal–Wallis test, followed by post hoc Bonferroni’s multiple comparisons, was used to test differences in the seasonal H and C that were obtained directly from the hourly acoustic data during chorusing hours, as well as the H and C obtained for the seasonal SPL and the seasonal forecast skill. The statistical calculations were performed using the R package “agricolae” 75. More

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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

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

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This work formulates a general framework of the WFE Nexus at the national level, which includes all pertinent interactions between water, food, and energy sources and demands. Figure 1 depicts the feedbacks involving resource availability and consumption. The causal loops of the developed model for national-scale assessment are shown in Fig. 2. The model depicted in Fig. 2 proposes reducing consumption to reduce the water crisis to the extent possible. By reducing water use and pollution the environmental water requirement can be reduced, thus alleviating the water crisis. This paper’s objective is sustainable management by reducing per capita water use (in the residential section) and per capita energy use (in the domestic, public, and commercial section). The WFE nexus is modeled as a dynamic system for demand management applied to the stocks of energy, surface water, and groundwater resources to calculate their input and output rates (flows) at the national level while providing for environmental flow requirements (Fig. 3). The national modeling approach is of the lumped type, meaning that inputs and outputs to the stocks of water and energy represent totals over an entire country (in the case study, Iran); therefore, the models does not consider intra-country regional variations. The units of water resources and energy resources are expressed in cubic meters and MWh, respectively.Figure 1Feedbacks between resources and uses in the WFE nexus taking into account environmental considerations.Full size imageFigure 2The causal loops of the model developed for simulating the WFE nexus.Full size imageFigure 3Flow diagram of the WFE Nexus system.Full size imageBalance of water resourcesThe study of water exchanges in a country is based on the law of conservation of matter. The following sections present calculations pertinent to the annual balance of surface and groundwater resources.Surface water resourcesThe national runoff generated in a country’s high-elevation areas (or high terrain) and low-elevation areas (plains) is quantified with the following equations:$${preheight}_{t}=HeightCotimes {Precipitation}_{t}$$
(1)

in which ({preheight}_{t}) = volume of precipitation that falls in high-elevation areas during period t, (HeightCo) = the percentage of total precipitation that falls in high-elevation areas, and ({Precipitation}_{t}) = volume of precipitation during period t.$${preplain}_{t}=PlainCotimes {Precipitation}_{t}$$
(2)

in which ({preplain}_{t}) = volume of precipitation that falls in the plains during period t, and (PlainCo) = the percentage of total precipitation that falls in plains (low elevation areas).$${SInflow}_{t}=HeighSInflowCotimes {preheight}_{t}+PlainSInflowCotimes {preplain}_{t}+{OutCSW}_{t}+{Dr}_{t}$$
(3)

in which ({SInflow}_{t}) = the total volume of surface flows during period t, (HeighSInflowCo) = the runoff coefficient in high-elevation areas, (PlainSInflowCo) = the runoff coefficient in the plains, ({OutCSW}_{t}) = the difference between the volume of surface inflow and outflow through a country’s border during period t; and ({Dr}_{t}) = the flow of groundwater resources to surface water resources (i.e., baseflow) during period t.It is possible to calculate the water use after calculating the annual surface water originating by precipitation. Some of the water use by the agricultural, industrial, and municipal sectors becomes return flows. Equations (4) through (9) show how to calculate the surface water use and the water return flows to the surface water sources.$${DomWD}_{t}={Population}_{t}times PerCapitaWatertimes 365$$
(4)

in which ({DomWD}_{t}) = the volume of water use in the municipal sector during period t, ({Population}_{t}) = the population of the country during period t, and (PerCapitaWater) = per capita drinking water use (cubic meters per person per day).$${IndDomWD}_{t}={DomWD}_{t}+{IndWD}_{t}$$
(5)

in which ({IndDomWD}_{t}) = the volume of water use in the municipal and industrial sectors during period t, and ({IndWD}_{t}) = the volume of water use in the industrial sector during period t.The water use by the agricultural sector accounts for the water footprint of agricultural products, which measures their water use per mass of produce, and adjusting the water use by including water losses and agricultural return flows. A separate sub-agent (AGR agent) is introduced to perform the calculations related to the agricultural sector to simplify the dynamic-system model (main model), and the required outputs (BWAgr, GWAgr) of the dynamic system model are called by the agent in the main model (see Figs. 3 and 4). The BWAgr is given by the expression within parentheses in Eq. (6).Figure 4Agricultural subsystem modeled in the AGR agent (shows how to calculate the blue and gray water footprints of agricultural products).Full size image$${AgrWD}_{t}=left(sum_{iin A}{BW}_{i}times {Product}_{i,t}right)times frac{1}{{E}_{Agr}}+OtherAgrWD$$
(6)

in which ({AgrWD}_{t}) = the volume of agricultural water use during period t, ({BW}_{i}) = blue water footprint of agricultural product i (cubic meters per ton), ({Product}_{i,t}) = the amount of production of agricultural product i during period t (tons), ({E}_{Agr}) = the overall irrigation efficiency, (OtherAgrWD) = the volume of water consumed by agricultural products not included in the set A of agricultural products (in cubic meters). The set A includes those agricultural products with the largest yields and shares of the national food basket.$${AgrReW}_{t}={AgrWD}_{t}times AgrReCo$$
(7)

in which ({AgrReW}_{t}) = the volume of water returned from agricultural water use during the period t, and (AgrReCo) = the coefficient of water returned from agricultural water use.$${IndDomReW}_{t}={IndDomWD}_{t}times IndDomReCo$$
(8)

in which ({IndDomReW}_{t}) = the volume of water returned from industrial and municipal water use during period t, and (IndDomReCo) = the coefficient of water returned from industrial and municipal water uses.$${ReSW}_{t}=IndDomReSWCotimes {IndDomReW}_{t}+AgrReSWCotimes {AgrReW}_{t}$$
(9)

in which ({ReSW}_{t}) = the volume of water returned from water uses to surface water resources during period t, (IndDomReSWCo) = the percentage of water returned from municipal and industrial water use to surface water resources, and (AgrReSWCo) = the percentage of water returned from agricultural water use to surface water resources.Water is applied to produce energy, and Eqs. (10) through (15) perform the related calculations. The ({WEIF}_{t}) variable in Eq. (14) is necessary to account for the volume of water saved as a result of the energy savings. A PR model is introduced to account for such water savings (see Fig. 3).$${Diff}_{t} ={OutputE}_{t}-{OutputE}_{t}^{P}$$
(10)

in which ({Diff}_{t})= the difference between the energy used in the main model during period t and the energy used in period t in the PR model, ({OutputE}_{t}) = the sum of energy uses during period t in the main model (the method of calculating ({OutputE}_{t}) is described in detail in “Energy uses”), and ({OutputE}_{t}^{P}) = the sum of energy uses during period t in the PR model. Equations (11) and (12) account for the case when energy use exceeds energy production under current conditions, in which case energy exports are reduced. This prevents additional energy production to meet excess demand, and, consequently, there would not be increases in water use.$${Diff}_{t} le 0,,,{if,,func}_{t}=0$$
(11)
$${Diff}_{t} >0,,,{ if,,func}_{t}={Diff}_{t}$$
(12)

in which ({ iffunc}_{t}) = the amount of energy saved during period t.Equation (13) calculates the water required to produce energy:$${{TotalWE}_{t}=Coal}_{t}times ENwateruseC+{Gas}_{t}times ENwateruseG+{OilPetroleumP}_{t }times ENwateruseO+{Nuclear}_{t}times ENwateruseN+{Elec}_{t}times ENwateruseE$$
(13)

in which ({TotalWE}_{t}) = the volume of water required to produce the energy demand during period t,({Elec}_{t}) = the amount of electricity production during period t (MWh), and (ENwateruseE) = the water required per unit of energy generated by electricity (cubic meters per MWh), all other terms were previously defined.Equation (14) calculates the water savings:$${WEIF}_{t}=sum_{t=1}^{T}frac{{TotalWE}_{t}}{{OutputE}_{t}^{0}}times {if,,func}_{t}$$
(14)

in which ({WEIF}_{t})= the volume of water saved as a result of the energy saved during period t, T = the number of periods of simulation (T = 5 years).Part of the water used to produce energy from coal, oil, petroleum products, and nuclear fuel is accounted for in the industrial sector water use. For this reason, the volume of water to produce energy calculated with Eq. (15) is reduced by that part of water already accounted for in the industrial water use to avoid double accounting.$${WE}_{t}={Coal}_{t}times ENwateruseC+{Gas}_{t}times ENwateruseG+{OilPetroleumP}_{t }times ENwateruseO+{Nuclear}_{t}times ENwateruseN-INDEtimes {IndWD}_{t}-{WEIF}_{t}$$
(15)

in which ({WE}_{t}) = the volume of water required to produce different types of energy (except those included in the industrial sector) during period t, ({Coal}_{t}) = the energy produced with coal during period t (MWh), (ENwateruseC) = the water required per unit of energy produced with coal (cubic meters per MWh),({Gas}_{t}) = the amount of energy produced with natural gas during period t (MWh), (ENwateruseG) = the water required per unit of energy produced with natural gas (cubic meters per MWh), ({OilPetroleumP}_{t}) = the amount of energy produced with crude oil and other petroleum products during period t (MWh), (ENwateruseO) = the water required per unit of energy produced with crude oil and petroleum products (cubic meters per MWh),({Nuclear}_{t}) = the amount of nuclear energy produced during period t (MWh), (ENwateruseN) = the water required per unit of nuclear energy produced (cubic meters per MWh), and (INDE) = the percentage of industrial water use already accounted for in Eq. (5) (which pertains to water used in the coke coal, oil refineries, and nuclear fuel industries).Part of the discharge of springs enters the surface water sources, and this enters the calculation of the input to the surface water-resources stock in Eq. (16):$${InputSW}_{t}= SInflow+{ReSW}_{t}{+ Fountain}_{t}$$
(16)

in which ({InputSW}_{t}) = the volume of inflow water to surface water sources during period t, and ({Fountain}_{t}) = discharge of springs to surface water sources during period t, other terms previously defined.The output of the surface water resources includes water use and the infiltration of surface water into groundwater, the latter calculated with Eq. (17):$${SInflowInf}_{t}={SInflow}_{t}times SInflowInfCo$$
(17)

in which ({SInflowInf}_{t}) = the infiltration volume of surface water during period t, and (SInflowInfCo) = the infiltration coefficient of surface water.The output of the surface water resources stock is calculated using Eq. (18):$${OutputSW}_{t}={AgrSWDCo}_{t}times {AgrWD}_{t}+{IndSWDCo}_{t}times {IndWD}_{t}+{DomSWDCo}_{t}times {DomWD}_{t}+{mathrm{ WE}}_{t}+{SInflowInf}_{t}-{EvSwSea}_{t}$$
(18)

in which ({OutputSW}_{t}) = the output volume of surface water during period t, ({AgrSWDCo}_{t}) = the percentage of gross agricultural water use from surface water resources during period t, ({IndSWDCo}_{t}) = the percentage of industrial water use from surface water resources during period t, ({DomSWDCo}_{t})= the percentage of gross drinking water consumption from surface water sources during period t, and ({EvSwSea}_{t}) = the total volume of evaporation from surface water plus the discharge of surface water to the sea during period t.The balance of surface water resources is calculated based on Eq. (19):$$SWaterleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[{InputSW}_{t}left(Sright)-{OutputSW}_{t}(S)right]dt+SWater(0)$$
(19)

in which (SWaterleft(tright)) = the stock of surface water resources at time t, (SWater(0)) denotes the stock of surface water at the initial time (t = 0).Groundwater resourcesGroundwater resources gain water from deep infiltration of precipitation in the plains and elevated areas from (1) inflows from outside of the study area, (2) infiltration from surface flows and return waters. Groundwater output factors also include the discharge of groundwater resources (wells, springs, and aqueducts), groundwater flow that moves outside the study area and evaporation. Infiltration of precipitation in the plains and in high terrain into groundwater resources is calculated with Eq. (20):$${Inf}_{t}=PrePInfCotimes {preplain}_{t}+PreHInfCotimes {preheight}_{t}$$
(20)

in which ({Inf}_{t}) = the volume of water entering groundwater sources through infiltration of precipitation during period t, (PrePInfCo) = the infiltration coefficient of precipitation in the plains, and (PreHInfCo) = the infiltration coefficient of rainfall in high terrain.Equation (21) calculates the volume of return water that accrues to groundwater resources:$${ReGW}_{t}=IndDomReGWCotimes {IndDomReW}_{t}+AgrReGWCotimes {AgrReW}_{t}$$
(21)

in which ({ReGW}_{t}) = the volume of water returned from water use that accrues to groundwater resources during period t, (IndDomReGWCo) = the percentage of water returned from municipal and industrial water use accruing to groundwater resources, and (AgrReGWCo) = the percentage of water returned from agricultural water use accruing to groundwater resources.The volume of groundwater input is calculated with Eq. (22):$${InputGW}_{t}={Inf}_{t}+{ReGW}_{t}+{SInflowInf}_{t}+{OutCGw }_{t}$$
(22)

in which ({InputGW}_{t}) = the volume of groundwater input during period t, and ({OutCGw }_{t}) = the difference between the volume of groundwater leaving and that entering the country during period t.The volume of groundwater output is calculated with Eq. (23):$${OutputGW}_{t}={AgrGWDCo}_{t}times {AgrWD}_{t}+IndGWDCotimes {IndWD}_{t}+DomGWDCotimes {DomWD}_{t}+{EvGwDr}_{t}$$
(23)

in which ({OutputGW}_{t}) = the volume of groundwater output during period t, ({AgrGWDCo}_{t}) = the percentage of gross agricultural water use from groundwater resources during period t, IndGWDCo = the percentage of industrial water use from groundwater resources during period t, DomGWDCo = the percentage of municipal water use from groundwater resources during period t, and ({EvGwDr }_{t}) = the total volume of evaporation from groundwater plus the drainage of groundwater resources to surface water resources at time t.Equation (24) calculates the annual balance of groundwater resources:$$GWaterleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[{InputGW}_{t}left(Sright)-{OutputGW}_{t}left(Sright)right]dt+GWater(0)$$
(24)

in which GWater(t) = the groundwater resources stock at time t, (GWater(0)) denotes the stock of groundwater at the initial time (t = 0).Energy usesEnergy uses are calculated with Eqs. (25)–(27). The total national energy use includes the agricultural, industrial, transportation, and exports sectors’ energy demands. The energy uses by these sectors do not change during the implementation of the policy, and, consequently do not change the WFE Nexus in that period; therefore, they are not included in the calculations.$${WDTP}_{t}={DomWD}_{t}times {CEIntensity}_{t}$$
(25)

in which ({WDTP}_{t}) = the energy used in the extraction, transmission, distribution, and treatment of water in the water and wastewater system during period t, and ({CEIntensity}_{t}) = the energy intensity in the extraction, transmission, distribution, and treatment of water in water and wastewater systems during the period t (MWh per cubic meter).$${ResComPubED}_{t}=ResComPubPerCapitatimes {Population}_{t}$$
(26)

in which ({ResComPubED}_{t}) = the energy use by the domestic, commercial, and public sectors during period t, and (ResComPubPerCapita) = the per capita energy consumption by the domestic, commercial, and public sectors (MWh per person per year).$${OutputE}_{t}={ResComPubED}_{t}+{WDTP}_{t}$$
(27)
Environmental water needsThe gray water footprint is defined as the volume of freshwater that is required to assimilate the load of pollutants based on natural background concentrations and existing ambient water quality standards. The estimation of the gray water footprint associated with discharges from agricultural production is based on the load of nitrogen fertilizers, which are pervasive in agriculture. The gray water footprint in terms of nitrogen concentration has been estimated by Mekonnen and Hoekstra24,25, as written in Eq. (28):$${GW}_{t}^{Agr}=sum_{iin A}{GW}_{i}times {Product}_{i,t}$$
(28)

in which ({GW}_{t}^{Agr})= the volume of gray water in the agricultural sector during period t, and ({GW}_{i}) = the volume of gray water associated with the production of one ton of agricultural product i (cubic meters per ton)(.)There are no accurate estimates of the concentrations of pollutants per unit of industrial production, or of the concentration of pollutants in municipal wastewater. Therefore, the conservative dilution factor (DF), which is equal to 1 for untreated returned water from the municipal and industrial sectors, is applied in this work. Equation (29) is a simplified equation of the gray water footprint26. The fraction appearing on the right-hand side of Eq. (29) is equal to the DF.$${GW}_{t}^{IndDom}= frac{{C}_{eff}-{C}_{nat}}{{C}_{max}-{C}_{nat}}times {IndDomReW}_{t}times IndDomReUT$$
(29)

in which ({GW}_{t}^{IndDom}) = the gray water footprint of the municipal and industrial sectors during period t, ({C}_{eff}) = the nitrogen concentration in return water (mg/L), ({C}_{nat}) = the natural concentrations of contaminant in surface water (mg/L), ({C}_{max}) = the maximum allowable concentration contaminant in surface water (mg/L), and (IndDomReUT) = the percentage of untreated returned water from the municipal and industrial sectors.The total gray water footprint is obtained by summing the footprints associated with the municipal/industrial and agricultural sectors:$${TotalGW}_{mathrm{t}}={GW}_{t}^{IndDom}+{GW}_{t}^{Agr}$$
(30)

in which ({TotalGW}_{mathrm{t}}) = the volume of gray water from all sectors during period t.This work considers qualitative and quantitative environmental water needs. Equation (31) is used to calculate the total environmental water need. The Tennant method for calculating the riverine environmental flow requirement (or instream flow) stipulates that, based on the conditions of each basin, between 10 to 30% of the average long-term flow of rivers represents the environmental flow requirement27. The sum of these requirements across all the basins equals the environmental requirement of the entire region or country. Yet, by providing 10 to 30% of the average long-term flow of rivers the riverine ecosystem barely emerges from critical conditions, and is far from optimal ecologic functioning. The total environmental water need is equal to the sum of the environmental flow requirement plus the volume of water needed to dilute the contaminants entering the surface water sources:$${ENV}_{t}={TotalGW}_{t}+Tennant$$
(31)

in which ({ENV}_{t}) = the environmental flow requirement during period t, and Tennant = the environmental flow requirement calculated by the Tennant (1976) method.The policy evaluation indexThe available renewable water is calculated with Eq. (32):$${IN}_{t}={OutCGW }_{t}+ {SInflow }_{t}+{ Inf}_{t}-{EvGwDr}_{t}$$
(32)

in which ({IN}_{t})= the renewable water available before the application of environmental constraints during period t.The volume of manageable water is calculated with Eq. (33):$$REWleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[INleft(tright)-ENVleft(tright)right]dt$$
(33)

in which REW (t) = the (cumulative) manageable and exploitable renewable water in the period t-t0.Equation (34) calculates the total water withdrawals by the agricultural, industrial, municipal, and energy production sectors:$${WDW}_{t}={OutputSW }_{t}+ {OutputGW}_{t}- {cheshmeh}_{t}$$
(34)

in which ({WDW}_{t}) = the sum of the withdrawals by the agricultural, industrial, municipal, and energy production sectors during period t.The cumulative water withdrawals are calculated with Eq. (35):$$withdleft(tright)=underset{{t}_{0}}{overset{t}{int }}WDWleft(tright)dt$$
(35)

in which (withdleft(tright)) = the sum of the withdrawals by the agricultural, industrial, municipal and energy production sectors in the horizon t-t0.Equation (36) calculates the water stress index:$${index}_{{t}_{f}}^{MRW}=frac{withd({t}_{f})}{REWleft({t}_{f}right)}times 100$$
(36)

in which ({index}_{{t}_{f}}^{MRW}) = the renewable water stress index at the end of the study period, and ({t}_{f}) = the period marking the end of the study horizon.Once the water and energy model is developed it must be calibrated with observational data prior to its use in predictions, as shown below. More

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