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We took as a starting point the hake – sole – Norway lobster Bay of Biscay ISIS-Fish database used for COSELMAR project16,20 (see http://isis-fish.org/download.html section “Bay of Biscay scenario dataset”, Database V0 in Fig. 1). This database was built using 2010 data, and was not calibrated, as it was designed for a geo-foresight study. Since our aim was to describe the system over a decade and simulate realistic dynamics close to available observations to assess management measures, we needed to update the parametrisation and calibrate the database. We took 2010–2012 as the calibration period, and 2013–2020 as the simulation period (grey arrow Fig. 1). The database has a monthly temporal resolution (constrained by the ISIS-Fish framework) and the spatial scale was set to match ICES statistical rectangles (0.5° latitude by 1° longitude rectangles, defined by the International Council for the Exploration of the Sea (ICES) https://www.ices.dk/data/maps/Pages/ICES-statistical-rectangles.aspx), consistent with available knowledge and data.In this section, we firstly describe all the data sources used to update and calibrate the database. Then, for each main component of an ISIS-Fish database – i.e. populations, exploitation and management – we describe this paper’s database parameters and assumptions. We finally describe the calibration procedure (inspired by previous work21,22), of which some results are shown in the Technical Validation section. We summarized this workflow in Fig. 1.Data sourcesData sources, estimates, and literature (including grey literature) were needed to update and calibrate the model. They are marked in Fig. 1 with salmon (data sources and estimates) and mustard (literature) blocks:

SACROIS23: French landings and effort logbook declarations for 2010 were made available at the log-event*commercial category*ICES statistical rectangle*population scale. It was used to design exploitation features of the database, as well as populations spatial structure.

LANGOLF survey: 2006–2010 LANGOLF surveys observations for 2006–2010 were made available for Norway lobster. They were used to work on Norway lobster abundance per length class and sex.

Intercatch: catch observations for 2010–2020 in the Bay of Biscay for hake, at the quarter-métier group scale, and catch observations per class for sole on 2010–2012, and 2010 Norway lobster catch observations per sex and length class24, used to describe the inter-annual effort dynamics, to calibrate and validate the model.

Estimates of hake abundance per size class in 2010, and hake quarterly estimates of recruitment on 2010–2012 from a northern hake spatial stock assessment model21, used to inform hake biology assumptions (named Other 1 in Fig. 1).

ICES WGBIE24 2010 estimates of abundance per class (sole and Norway lobster), to inform their abundance at the initial time step; 2010–2012 yearly fishing mortality estimates per age class (sole) to calibrate the database (named Other 2 in Fig. 1).

Other population, exploitation and management assumptions were informed with scientific literature25 and grey literature26,27 (Literature block in Fig. 1).

Management assumptions were informed with legal texts2,4,28,29,30,31,32,33,34 and reported quota values in working group reports24.

About populationsThis section describes for each species the assumptions and parameters values, except for accessibility, which has been calibrated, as described in section Calibration procedure. For all assumptions and values, more details are provided in Supplementary Information’s section 2.2.HakeThe stock size structure was defined with 1 cm size bins for [1;40[cm individuals, 2 cm for [40;100[cm individuals, and 10 cm for [100;130+] cm individuals35. Areas of presence were defined based on 2010 SACROIS French landings data per commercial category and statistical rectangle23, leading to the definition of a presence, a recruitment, an interim recruitment and a spawning area25 (see Supplementary Information’s section 2.2 and Figure S1). These areas allow for the description of intra-Bay of Biscay migrations related to spawning and recruitment processes: mature individuals aggregate at the beginning of the year on the shelf break to spawn, and then disperse on the shelf36,37,38,39,40 (at the beginning of April and July in the model). Also, from age 1 (around 20 cm), individuals in recruitment zone spread in interim recruitment zone, to model a diffusion towards areas neighbouring the nursery area, at the beginning of each time step (see Supplementary Information’s section 2.2 and Table S11). Maturity-at-size and weight-at-length relationships were the same functions as used by ICES working group35,41. Natural mortality was fixed at 0.5, basing on preliminar runs, instead of the commonly used 0.442. Recruitment values were defined prior to the simulation for 2010–2020 using available estimates on the 2010–2015 time series21,27. Deterministic estimates from these sources were allocated to the recruitment area in the Bay of Biscay and the beginning of each month in January-September on the whole time series, of which values are provided in the Supplementary Information’s section 2.2 and Table S3. Growth is modelled through monthly growth increments5,25. However, given the different widths of size bins in the implemented size structure, a correction was provided to values in the transition matrix to eliminate artifacts when growing to a size bin wider than the size bin of origin, as detailed in Supplementary Information’s section 2.2. Abundance at the initial step in each zone was estimated from Bay of Biscay abundance estimates for 201021. Mature individuals over 20 cm were allocated to the spawning area, all individuals strictly shorter than 20 cm were allocated to the recruitment area (as they were assumed to be less than 1 year old), and remaining individuals were allocated to the interim recruitment area. None were allocated to the presence area, in which individuals will go later in the time series, after disaggregating from the spawning area25 (Table S13).SoleThe stock is age structured, with 7 classes going from ages 2 to 7+43 (Table S2). No seasonal variations were implemented. Only a single presence zone was defined (see Supplementary Information’s section 2.2 and Figure S1), as in preliminary runs defining more presence areas for sole did not yield more knowledge in this study. We implemented ICES working group values for natural mortality, weight-at-age (Table S1) and maturity-at-age43. Recruitment occurs at the beginning of each year, individuals being recruited at age 2 (ages 0–1 were not modelled; Table S4). We implemented ICES working group estimates27 for abundance at initial time step (Table S14).Norway lobsterThe stock has a sex-size structure, with 1 mixed recruitment class at 0 cm; 33 length classes for males at 2 carapace length mm intervals, from [10;12[to [72;74[carapace length mm; 23 length classes for females at 2 carapace length mm intervals, from [10;12[to [52;54[carapace length mm. A single presence area was defined: the Great Mudbank21 (see Supplementary Information’s section 2.2 and Figure S1). Several seasonal processes occur for this stock, impacting recruitment, accessibility and growth: 1/ January, begins with the annual recruitment. Females are inside their burrows, less accessible; 2/ February-March females are inside their burrows, less accessible; 3/ April: Spring moulting, females are more accessible; 4–5/ May-August females are more accessible; 6/ September, females are inside their burrows, less accessible; 7/ October: Autumn moulting only for immature females and all males, females are inside their burrows, less accessible; 8/ November-December, females are inside their burrows, less accessible44. We implemented ICES working group values for natural mortality, weight-at-class and maturity-at-class45,46,47. Growth occurs twice a year, when moulting in April and October, and is modelled with growth increments. Recruitment occurs at the beginning of each year, modelled with a Beverton-Holt relationship26, and was assumed to have the same spatial distribution as spawning stock biomass. Abundance at initial step was derived from LANGOLF survey observations and ICES WGBIE estimates25,26 (Table S16).About exploitationThe fishing exploitation structure (fleets, strategies, métiers and gears) were derived following a classification method on SACROIS 2010 landings and effort data13,23 from French fleets, and taken from a TECTAC project (https://cordis.europa.eu/project/id/Q5RS-2002-01291) database for Spanish trawlers. More details on their definition are provided in Supplementary Information’s section 2.3, Tables S5–S9 and S20–S21 and Figure S3. Spanish longliners and gillnetters fleets exploitation was described based on catch (observations from Intercatch48) rather than effort.Hake selectivity and discarding functions (one for each gear) were taken from estimates of a spatial hake stock assessment model21. Parameters values and formulæ are provided in Supplementary Information’s section 2.3 and Tables S6-S7. On top of this, inter-annual fleet dynamics factors were included in equation (21) of ISIS-Fish documentation8 in order to account for observed catch temporal variations. These factors are therefore multiplicative parameters of the target factor of each species for each métier. They are computed using observed catch27 and differ according to the period and targeted species:

over 2010–2016, it is a ratio of observed catch in weight per year over catch observations for 2010: for hake, one per métier *season*year (left(frac{ObservedCatc{h}_{metier,season,year}}{ObservedCatc{h}_{metier,season,2010}}right)), for sole, one per métier *year (left(frac{ObservedCatc{h}_{metier,year}}{ObservedCatc{h}_{metier,2010}}right)), and for Norway lobster, one per year (identical for each métier catching Norway lobster) (left(frac{ObservedCatc{h}_{year}}{ObservedCatc{h}_{2010}}right));

over 2017–2020: at the time of writing these assumptions, more recent data was not available, and ratios were deduced from trends on 2014–2016. A linear model was fitted on ratios deduced earlier on 2014–2016. If a significant trend was identified (hake: whitefish trawlers quarters 2 and 4, longliners and gillnetters seasons 2–3; sole and Norway lobster: all métiers), the slope was used to deduce 2017–2020 ratios (the slope was halved for hake whitefish trawlers and sole and Norway lobster values to avoid unrealistic high values of effort). Otherwise, 2016 ratios were used.

All values are provided in Supplementary Information’s section A.2 Tables S22–S24, and the final values of target factors are derived from the Calibration procedure.About managementWe implemented a set of management rules close to what is currently implemented in the Bay of Biscay.All stocks are managed by TALs (Total Allowable Landings) until 2015 and then by TACs (Total Allowable Catch), except for Norway lobster, managed by TALs on the whole time series, not being under the landings obligation. To favour a better parametrisation, allowing for more reliable dynamics on the following years of the time series, no TALs were implemented during the calibration period (2010–2012; Fig. 1). These regulations were implemented from 2013 using historically TALs and TACs values24.Landings of the three stocks are also constrained by a Minimum Conservation Reference Size regulation that was implemented for all stocks using values currently enforced in the studied fishery28. Likewise, from 2016, the Landings Obligation was implemented, with de minimis exemptions for hake and sole, depending on the year and the gear used to fish them2,31,32,33,34. See Supplementary Information’s sections 2.4 and A.3, Figure S2 and Table S10 for further details on these restrictions.In response to the above management rules, a fishers’ behaviour algorithm has been developed to describe fishermen adaptation. Some métiers may be forbidden, depending on some conditions – the catch quota has been reached, the landings obligation is enforced – but also some values – the proportion of discarded catch, and also catch on previous years. Therefore fishermen change métiers within their strategy métiers set through a re-allocation of fishing effort to the latter set. This re-allocation aims to avoid quota overshooting. Further details about this algorithm are provided in the Supplementary Information’s sections 2.4 and A.3 and Figure S2.Calibration procedureThe model has been calibrated using two parameters (population accessibility and fishing target factor) involved in the catchability process (equation (21) in ISIS-Fish documentation8). The objective of the calibration is to reproduce the dynamics of catch over 2010–2012 at the species*métiers group scale, for each year or quarter depending on available data’s granularity. Calibration is sequentially performed: accessibility parameters for each population were estimated first followed by the target factors. The estimation of each parameter set (parameter type * population) combination was separated, and values were estimated jointly within each parameter set. To account for the specificity of each population model dynamics (global age-based for sole, spatial and size-based for hake, spatial, sex and size-based for Norway lobster), an objective function is defined for each population to calibrate their accessibility. More details on objective functions and procedures are provided in Supplementary Information’s section 2.5, as well as estimated values in Tables S17–S19.Hake accessibilityThe calibration for hake accessibility is based on a procedure developed for a former version of the database25. One parameter was estimated per quarter, all values being equal across length classes. The model outputs were fitted to hake catch observations in weight in the Bay of Biscay in 2010–2012 per length class.Sole accessibilityOne parameter was estimated per age class. The model outputs were fitted to WGBIE fishing mortality per age class for sole27 in 2010–2012.Norway lobster accessibilityOne parameter was calibrated per sex and length class. The model outputs were fitted to catch in numbers per length class and sex in 2010 per quarter provided by WGBIE.About target factorsTarget factors drive how the effort is distributed between populations, métiers and season*year combinations. They were split in 3 components: a fixed component derived from the SACROIS effort dataset analysis (Tables S25–S27), another fixed component driving inter-annual variations of fishing effort (Tables S22–S24), derived from catch observations, and finally an estimated component (Tables S28–S30), allowing to tune the model’s dynamics to observed catch. This section focuses on the estimation of the latter.Hake target factors20 parameters were defined, for each combination of the 5 groups of métiers (longliners, gillnetters, whitefish trawler (coastal), whitefish trawler (not coastal), Norway lobster trawler, see definition Table S8) and 4 quarters. We fitted the model’s outputs to the same data and with the same objective function as for hake’s accessibilities estimation.Sole target factors1 estimated component per group of métiers (gillnetters, Norway lobster trawlers and whitefish trawlers) and quarter. We fitted the model’s outputs to sole catch in weight on 2010–2012 for each métier and quarter.Norway lobster target factors1 estimated component per group of métiers (Norway lobster trawlers and whitefish trawlers). We fitted the model’s outputs to monthly Norway lobster landings data per length and sex class for 2010.Base simulationThe base simulation ran from January 2010 to December 2020 inclusive, with a monthly time step, using the database and parameters values described in this document. Several outputs of interest may be explored after a run: catch (discards and landings), as done in several figures in this paper, but also biomass (total biomass or mature biomass), fishing mortality values, or effort, all at a fine spatio-temporal scale. More

Experimental siteAll procedures for the experiment involving animals were carried out in accordance with relevant guidelines and regulations and they were approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Florida (protocol #201509019). The experiment was conducted at the University of Florida North Florida Research and Education Center (NFREC) located in Marianna, FL (30° 52ʹ N, 85° 11ʹ W, 35 m asl) during 2018 and 2019.The study site was an existing mixed RP-bahiagrass grazing study where ‘Ecoturf’ RP was strip-planted into ‘Argentine’ bahiagrass on 12 June 2014. Rhizoma peanut strips were approximately 2-m wide, making it possible to harvest RP forage, roots, and rhizomes free of bahiagrass contamination3,4. The RP was collected from a nursery at the University of Florida—NFREC, whereas the bahiagrass seeds were bought from a seed company. All plants were collected, purchased, managed, and the research was conducted in compliance with relevant institutional, the corresponding national, and international guidelines and legislation.Soils at the experimental site were classified as Orangeburg loamy sand (fine-loamy, kaolinitic, thermic Typic Kandiudults24. At the beginning of the study, soil pH was 5.7 and soil OM was 15.4 g kg−1. Additionally, Mehlich-I extractable soil P, K, Mg, and Ca concentrations at the beginning of the experiment were 26, 99, 43, and 224 mg kg−1, respectively. Total annual rainfall and average annual temperature at the experimental site were 1889 and 602 mm, and 19 and 21 °C, for 2018 and 2019, respectively, and their monthly averages are shown in Fig. 5.Figure 5Monthly weather conditions at North Florida Research and Education Center (NFREC) Marianna, FL, during the experimental years.Full size imageTreatments and experimental designTreatments were two defoliation regimes applied to RP, continuously stocking and 56-days interval between clipping harvests. At the continuous stocking, stocking rates were variable to maintain similar herbage allowance among pastures, which was assessed every 14 days as described by Sollenberger et al.25. Two tester Angus crossbred steers (Bos spp.) remained on each pasture throughout the experimental period. Put-and-take cattle were allocated as needed to maintain a target herbage allowance of 1.5 kg DM kg−1 bodyweight3. Treatments were situated adjacent to each other (i.e., paired sites) in monoculture strips of RP within each of three 0.85-ha pastures. Each pasture was considered a block, thus the experiment consisted of three replicates of each treatment in a randomized complete block design. Within each replicate, treatments had three repetitions (pseudo replicates). To prohibit animal access to the non-grazed treatment, three 2 × 2-m exclusion cages were placed on RP strips in each pasture. Rhizoma peanut herbage mass was determined at both the grazed and non-grazed sites three times each year, at days 56, 112, and 168 of the experimental period by using a 0.25-m−2 quadrat. Two quadrats were collected in each repetition by clipping all the biomass within each quadrat at 2-cm stubble height. After each herbage mass sampling, the non-grazed residual dry matter inside the cages was clipped to a 2-cm stubble height using a weed eater and the herbage removed by raking. On average, across sampling dates and years, herbage mass at the grazed and non-grazed sites was 1050 and 1810 kg of organic matter (OM) ha−1, respectively.Long-term and short-term decomposition studiesThere were two types of root-rhizome decomposition trials. The first is referred to as the long-term decomposition study, and the second is the short-term decomposition study. The long-term study had an incubation period of 168 days, with a single in-situ incubation per year starting in May. The short-term study had in-situ incubation periods of 56 days and there were three incubations per year, occurring in May, June, and August. In all cases, only roots and rhizomes attached to the plant were used in both trials.Long-term studyOn 26 Apr. 2018 and on 23 Apr. 2019, right after RP emergence after breaking dormancy, RP roots and rhizomes were collected from an existing mixed RP-bahiagrass grazing study where RP had been planted in strips into bahiagrass (Paspalum notatum Flüggé) in 2014. Rhizoma peanut strips were approximately 2.75-m wide, alternating with similar wide bahiagrass strips. A pure stand of RP had been maintained in the strips during previous years using herbicides3, making it possible to harvest RP roots and rhizomes free of bahiagrass contamination. Roots and rhizomes were collected at 24 different points in each of three blocks of the original experiment. Roots and rhizomes were collected at 20-cm depth using shovels. As defoliation treatments had not being applied at this time of the year, the same material was used to perform the incubation inside and outside the exclusion cages. After harvesting, excess soil was removed by shaking from the root-rhizome mat using a 1.4-cm diameter sieve. Thereafter, the existing aboveground material was clipped, and the roots and rhizomes were then washed over the same sieve to remove the remaining soil. After washing, roots and rhizomes were dried to constant weight in a forced-air drying oven at 55 °C.To perform the decomposition study, approximately 12 g of dry roots and rhizomes were placed in Ankom bags (10 by 20 cm, 50 µm porosity; ANKOM Technology) and sealed17. Roots and rhizomes were aimed to be placed intact into Ankom bags, nonetheless, when they could not fit inside the bags, they were cut in the middle before being placed. On 2 May 2018 and 1 May 2019, the incubation period began. For each treatment, bags were incubated in situ in the field at 10-cm depth in the same blocks from which they were collected. Bags were removed from the field after 0, 3, 7, 14, 28, 56, 112, and 168 days. For each treatment within each block, three bags were incubated for each incubation time. Additionally, empty bags (one bag per treatment per time per block) were placed in the field. After removal of the in-situ bags from the field, samples and empty bags were dried at 55 °C for 72 h, cleaned with a brush, and weighed. Thereafter, samples were ground to pass a 2-mm screen using a Wiley Mill (Model 4, Thomas-Wiley Laboratory Mill, Thomas Scientific) and analyzed for DM and OM. Subsamples of the 2-mm ground samples were ball milled in a Mixer Mill (MM 400, Retsch) at 25 Hz for 9 min. Ball-milled samples were analyzed for C and N by dry combustion using an elemental analyzer (Vario Micro cube, Elementar). Additionally, samples ground at 2-mm were used to determine ADF in aboveground samples26. The N concentration in the ADF was determined using the above protocol to obtain the ADIN.Short-term studiesThe short-term studies were performed following the same procedures as the long-term study, except that the incubation period was only 56 days, and these studies were repeated three times each year. Roots and rhizomes were incubated in situ on 2 May, 27 June, and 23 Aug. 2018 and on 1 May, 26 June, and 21 Aug. 2019, following the same protocol as described above, except that bags were removed from the field after 0, 3, 7, 14, 28, and 56 days of incubation. The incubations occurring in May, June, and August will be referred as early, middle, and late season, respectively.The early-season incubation period uses the data from the first 56 days of the long-term study described above. For the middle- and late-season incubations each year, roots and rhizomes were harvested approximately 7 days days prior to incubation. Approximately six points in each repetition were collected at 20-cm depth using shovels. For the grazed treatment, roots and rhizomes were collected in the grazed area nearby the exclusion cages, whereas for the non-grazed treatment, the material was collected inside the exclusion cages. After removal of the bags from the field, they were processed and analyzed for DM, OM, C, and N following the protocol described above.Statistical analysesLong-term studyRemaining biomass, remaining N, C:N ratio, ADF, and ADIN were analyzed using the PROC GLIMMIX from SAS27, with treatment and days of incubation as fixed effects, and years and blocks as random effects. Days of incubation were considered repeated measures. Means were compared using the PDIFF procedure at the 5% significance level. When treatment or the interaction of treatment × day of incubation were statistically significant in the ANOVA, nonlinear models were tested to fit the data for each variable and treatment. Nonlinear models were selected for a given response based on data distribution and type of response. If only days of incubation was significant, the same model was applied for all treatments.Remaining biomass (OM basis), remaining N, and C:N ratio were explained by the single exponential decay model14,17,28. The equation describing this process is:$$X=B0, {exp}^{-kt},$$
(1)
where X is the remaining biomass, remaining N, or C:N ratio at day t, B0 is the disappearance coefficient, and k is the relative decay rate (g g−1 day−1). The model used to describe ADF and ADIN was the two-stage model “linear plateau”15,29. The equation describing this process is:$$begin{gathered} Xt = A + b1 times t, {text{if t }} le {text{ T}}, hfill \ {text{and}},{ } Xt = A + b1 times T, {text{if t }} > {text{ T}}, hfill \ end{gathered}$$
(2)
where X is the concentration of ADIN, t is the day of incubation, A is the initial concentration, b1 is the rate of increase in concentration from the beginning of incubation until plateau is reached; and T is the day in which concentration reaches the plateau.Short-term studiesThe single exponential model was applied in the remaining OM and remaining N, for each experimental unit, to obtain individual values for B0 and k. The data for initial N concentration, initial C:N ratio, and B0 and k for remaining OM and remaining N were analyzed using the PROC GLIMMIX from SAS27, with treatment and period as fixed effects, and years and blocks as random effects. Means were compared using the PDIFF procedure at the 5% significance level.Arrive guidelinesThis is study is reported in accordance to ARRIVE guidelines. More

The water table depth, surface water body area, and surface ecological processes have all changed significantly during the 20 years the ecological water conveyance projects have been underway in the lower reaches of the Tarim River. Specifically, there has been a notable increase in the water table, surface water body area, vegetation density and coverage, the vegetation index (NDVI), Net Primary Production (NPP) of natural vegetation, and ecosystem function and health. The following sections provide details on these changes.Changes in groundwater table depthGroundwater (soil water) is the most important water source for maintaining natural vegetation in the lower reaches of the Tarim River, as the climate is extremely arid and atmospheric precipitation has little ecological significance. The changes in water table depth are directly related to the composition, distribution, and growth of the natural vegetation of the desert riparian forest, which in this case is mainly P. euphratica5. During the past 20 years, the ecological water conveyance in the lower reaches of the Tarim has been intermittent, and the groundwater table elevation has been closely related to the water conveyance. From the analysis of the groundwater table’s rise in the upper, middle, and lower reaches of the Tarim River (Fig. 1), the magnitude of the uplift is clearly related to four crucial factors: the groundwater table depth prior to the water conveyance, the volume of water discharge, the duration of the transfer, and the water head location.Figure 1Changes in groundwater depth of typical monitoring cross-sections pre- and post-conveyance of water in the lower reaches of Tarim River from 2000 to 2020. Yengsu, Karday, Argan and Yikanbujima are four monitoring sections in the lower reaches of Tarim River. “#1”is the No. 1 groundwater level monitoring well on each monitoring section, which is located 50 m away from the river.Full size imageIn the early stages of the water conveyance projects (2000–2010), the groundwater table in the upper and middle segments of the lower reaches of the Tarim River rose to a relatively large extent, while the groundwater table in the lower segment of the river only showed an increasing rising trend after 2011. The monitoring results reveal that after nearly 20 years of ecological water conveyance, the groundwater table in three sections of the lower reaches of the Tarim has been affected at a range of more than 1000 m. The three sections are the Yengsu section in the upper segment, the Karday section in the middle segment, and the Yiganbujima section in the lower segment. Furthermore, the groundwater table has risen by 2.69, 1.38 and 1.59 m, respectively, in these three sections22. Within 100 m from the river, the water table depth rose from 7.76, 9.31 and 7.82 m prior to ecological water conveyance to 3.70, 4.48, and 2.69 m, and 4.06, 4.83, and 5.13 m, respectively, after it. Within 500 m from the river, the water table rose by 1.6, 3.99, and 5.26 m, respectively. The shallow groundwater in the lower reaches of the Tarim River has also been recharged to a certain extent, and the lateral influence range is still gradually expanding.Changes in water body areaThe changes in water body area in the lower reaches of the Tarim River are closely related to the amount of water delivered via conveyance. During the past 20 years, the surface water body area, seasonal water body area, and permanent water body area all decreased to the lowest point in 2009, with the river water failing to reach Taitema Lake, the river’s terminal, in 2006, 2007, and 200923. The surface water body area, seasonal water body area and permanent water body area in the river’s lower reaches fluctuated and increased during the ecological water conveyance process. In particular, the seasonal water body area in the upstream section showed a significant expansion. The area increase rate of surface water, seasonal water, and permanent water in the middle section from Yengsu to Argan is 1.75 km2 a−1, 1.58 km2 a−1, and 0.16 km2 a−1, respectively. Similarly, the area of surface water bodies, seasonal water bodies, and permanent water bodies in the lower section (below Argan) increased at the rate of 13.48 km2 a−1, 8.24 km2 a−1, and 5.23 km2 a−1, respectively. It is worth mentioning that the area of surface permanent water body and seasonal water body in Taitema Lake significantly increased, with the area of the lake waters expanding 417.08 km2, from 38.19 km2 in 2000 to 455.27 km2 in 2019. This represents a nearly 12-fold increase (Fig. 2).Figure 2Spatial distribution of water surface area in lower reaches of Tarim River in 2000 and 2019. The subfigures were generated in R 4.0.2 (https://cran.r-project.org/bin/windows/), and then merged in Microsoft PowerPoint 2013 (https://www.microsoft.com/).Full size imageVegetation sample site monitoring analysisThe vegetation species in the lower reaches of the Tarim River were sparsely distributed, with P. euphratica and Tamarix sp. as the main established species. In the longitudinal direction, surface vegetation coverage and species number decreased as the water table depth increased from the upper and middle segments to the lower segment. In the lateral direction, surface vegetation shows the same trend, with groundwater table depth increasing the greater the distance from the river13.The surface ecological processes in the lower reaches of the Tarim River have responded positively to the water conveyance project, with density, coverage and the number and diversity of species significantly increasing. However, the response of surface ecological processes to the changes in groundwater table uplift has varied from section to section. In the lateral direction, the groundwater table in areas nearer to the river had a more prominent rise and the response of surface vegetation was stronger, whereas the groundwater table rise in areas farther from the river was smaller and so the response of surface vegetation was weaker. In the longitudinal direction, the same trend was observed from the upper to the lower segments in response to changes in the groundwater table. In this paper, we analyze the changes in detail by taking a closer look at the Yengsu section, which is located at the beginning of the middle section of the lower reaches of the Tarim River. In so doing, we apply sample site investigation and dynamic monitoring of the groundwater table to the study area.Changes in vegetation density and coverageThe results of our sample site monitoring show notable positive changes in groundwater depth between 2000 and 2021 as a direct result of the ecological water conveyance initiative. At 150 m from the river, the groundwater table depth rose from 8.47 m to 4.34 m, respectively, representing an uplift of 4.13 m (Fig. 3c). Moreover, the vegetation coverage and density increased from 18.77% and 0.016 plants/m2 to 46.51% and 0.049 plants/m2, and the number of species doubled from three to six.Figure 3Changes in vegetation coverage, density and number of species (a), species diversity indices (b), and groundwater depth (c) for each site at Yengsu section in the lower reaches of Tarim River.Full size imageAt 250 m from the river, the groundwater table depth rose from 8.07 m in 2000 to 4.85 m in 2021, representing an uplift of 3.22 m. The vegetation coverage and density increased from 10.89% and 0.020 plants/m2 to 31.24% and 0.160 plants/m2, respectively, and the number of species jumped from five to seven.At 350 m from the river, the water table rose 2.48 m between 2000 and 2021. The vegetation coverage and density increased from 3.69% and 0.010 plants/m2 to 22.27% and 0.022 plants/m2, respectively, and the number of species increased from two to three. It is worth noting that the expansion in vegetation cover in the first three sample sites was mainly due to the increase in the number and canopy width of herbs and shrubs that occurred as a direct result of the ecological water conveyance process.At 750 m from the river, the groundwater table depth rose from 5.96 m to 4.98 m between 2005 and 2021, respectively, representing an uplift of 0.64 m, while the vegetation coverage and density increased from 20.07% and 0.011 plants/m2 to 26.43% and 0.019 plants/m2, respectively.At 1050 m from the river, the sample site had an elevated water table of 1.22 m. The vegetation coverage and density increased from 2.41% and 0.004 plants/m2 in 2005 to 5.89% and 0.0148 plants/m2 in 2021, respectively (Fig. 3a). Among them, the increase in canopy area of Tamarix sp. and P. euphratica in the sample site was the main reason for the expansion in coverage.Changes in species diversity indicesPlant richness and evenness in the lower reaches of the Tarim River were low, with species diversity indices showing significant changes in response to the ecological water conveyance and the rise in the groundwater table (Fig. 3b). For example, at the Yengsu section, the Simpson dominance index, McIntosh evenness index and Margalef richness index, which reflect changes in species diversity, decreased from 0.58, 0.45 and 0.74 in 2005 to 0.46, 0.03 and 0.03, respectively. These changes occurred in response to the increase in groundwater depth from the first sample site at 150 m to the sixth sample site at 1050 m from the river channel. After 20 years of ecological water conveyance, the Simpson dominance index, McIntosh evenness index and Margalef richness index had increased on average by 0.33, 0.35 and 0.49, respectively, in the first three sample sites (Fig. 3b).Vegetation index (NDVI) changesThe Normalized Difference Vegetation Index (NDVI) is an important indicator of vegetation growth24. The study results reveal that the NDVI of the lower reaches of the Tarim River increased from 0.14 in 2000 to 0.21 in 2020, representing a rise of about 33.3%. The ecological water conveyance expanded the river region’s natural vegetation 188%, from 492 km2 in 2000 to 1423 km2 in 2020. Specifically, the area of low, medium, and high vegetation cover expanded by 277 km2, 537 km2 and 132 km2, representing increases of 20.8%, 448% and 190%, respectively. Further analysis of changes in vegetation coverage at different river sections indicate that the area of low vegetation coverage in the upper and middle segments showed a decreasing trend, whereas the area of medium and high vegetation coverage in the upper and middle segments showed an increasing trend. This latter trend was especially prominent in the middle segment, where the increase in the area covered by medium and high vegetation was relatively large.In the downstream segment, the area covered by all types of vegetation showed an upward trend, with the area covered by low vegetation expanding significantly (Fig. 4). In the lateral direction, the NDVI within 2 km of the water conveyance channel showed a more obvious response with greater increases, while NDVI beyond 2 km from the channel revealed smaller increases25. These differences reflect the influence range of the ecological water conveyance.Figure 4Variation of vegetation cover in the lower reaches of Tarim River. Spatial distribution of fraction of vegetation cover in (a) 2000, (b) 2010 and (c) 2020. Trends of (d) high fraction of vegetation cover, (e) middle fraction of vegetation cover and (f) low fraction of vegetation cover in different river sections. (g) Vegetation area and (h) change trend at different distances from the river.Full size imageChanges in net primary production (NPP) of natural vegetationNet primary production (NPP) is a key parameter of carbon cycling and energy flow in terrestrial ecosystems. NPP not only reflects terrestrial ecosystem productivity, but also characterizes the quality of terrestrial ecosystems and plays an important role in global change and carbon balance26,27. The results of our study show that the area of natural vegetation in the lower reaches of the Tarim River with highly significant and significant increases in NPP during the study period accounted for 31.93% (P  herbaceous community. The largest increase in NPP was observed in the Tamarix spp. community, rising 350.20% from 2001 to 201928.Area changes in vegetation carbon sink areaThe ecological water conveyance project in the lower reaches of the Tarim expanded the vegetation coverage and enhanced the carbon sequestration capacity of the region through photosynthesis. The lower reaches of the river are dominated by desert and sparse vegetation, and the ecosystem carbon sinks are mainly low carbon sinks. The monitoring results of the study show that the vegetation carbon sink area in the river’s lower reaches indicate a gradual expansion under the influence of the ecological water conveyance29, increasing from 1.54% of the study area in 2001 to 7.8% in 2020. As well, the Net Ecosystem Productivity (NEP) of the area’s vegetation showed an increasing trend at a rate of 0.541 g C·m−2·a−1, with the largest increase – 0.406 g C·m−2·a−1 – occurring in summer29and no significant carbon sink area in winter.Furthermore, in order to quantitatively investigate the degree of influence of ecological water conveyance on the carbon sink area in the lower reaches of the Tarim, a linear fit of cumulative water conveyance and carbon sink area was performed (Fig. 5). Based on the results, a strong linear correlation was found between cumulative water conveyance and carbon sink area (R2 = 0.958, p  More

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Geospatial dataSampling locations were established, flagged, and recorded in June 2016, using a Trimble Geo7X global navigation satellite system (GNSS) receiver using the Trimble® VRS Now real-time kinematic (RTK) correction. Location accuracies were verified to within ±2 cm. Points were imported into a geodatabase using Esri ArcMap (Advanced license, Version 10.5) and projected using the Universal Transverse Mercator (UTM), Zone 17 North projection, with the 1983 North American datum (NAD83). Field investigators navigated to the flagged locations by visually locating them in the field or by using recreational grade GNSS receivers with the locations stored as waypoints.Plant tissue sampling and preparationMiscanthus x giganteus grows in clumps of bamboo-like canes. A single cane was cut at soil level from each of the five sample collection points in each circular plot, individually labelled, and brought to the lab for processing (Fig. 2). Each stem was measured from the cut at the base to the last leaf node, and the length was recorded. Green, fully expanded leaves were cut from each stem and leaves and stems from each plant were placed in separate paper bags and dried at 60 °C. The dry leaf and stem tissues were ground to pass a 1 mm screen (Wiley Mill Model 4, Thomas Scientific, Swedesboro, New Jersey, USA). Subsamples of the ground material were analyzed for total carbon (C) and nitrogen (N), acid-digested for the analysis of total macro- and micronutrients, and water-extracted for spectroscopic analysis and the characterization of the water extractable organic matter (WEOM) (Fig. 2).Fig. 2Images of field samples, and diagram of plant tissue processing. Center panel – flow chart outlining the procedures for plant tissue processing, the kinds of analyses performed, and the type of data generated. Upper left inset panel – ground level picture of Miscanthus x giganteus circular plots. Upper right inset panel – some plant samples on the day of collection.Full size imageTotal carbon and nitrogenDried and ground leaf and stem material (~4–6 mg) was analyzed for total C and N content by combustion (Vario EL III, Elementar Americas Inc., Mt. Laurel, New Jersey, USA). The instrument was calibrated using an aspartic acid standard (36.08% C ± 0.52% and 10.53% N ± 0.18%). Validation by inclusion of two aspartic acid samples as checks in each autosampler carousel (80 wells) resulted in a net positive bias of 1.44 and 1.68% for C and N, respectively. The mean C and N concentrations and standard deviations for the sample set are presented in Table 1.Table 1 Giant miscanthus composition including leaf (L) and stem (S) dry weight, length, and carbon (C) and nitrogen (N) concentrations (n = 165). Values are reported as means ± standard deviations.Full size tableMacro- and micronutrientsPlant tissue samples were analyzed for a suite of macro- and micronutrients including aluminum (Al), arsenic (As), boron (B), calcium (Ca), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), potassium (K), magnesium (Mg), manganese (Mn), molybdenum (Mo), sodium (Na), nickel (Ni), phosphorus (P), lead (Pb), sulfur (S), selenium (Se), silicon (Si), titanium (Ti), vanadium (V), and zinc (Zn) using Inductively Coupled Plasma with Optical Emission Spectroscopy (ICP-OES). Samples (0.5 g) were digested using 10 mL of trace metal grade nitric acid (HNO3) in a microwave digestion system (Mars 6, CEM, Matthews, North Carolina, USA). During the digestion procedure (CEM Mars 6 Plant Material Method), the oven temperature was increased from room temperature to 200 °C in 15 minutes and held at 200 °C for 10 minutes. The pressure limit of the digestion vessels was set to 800 psi although it was not monitored during individual runs. Sample digestates were transferred quantitatively to centrifuge tubes, diluted to 50 mL with 2% HNO3 (prepared with lab grade deionized water), and centrifuged at 2500 rpm for 10 min (Sorvall ST8 centrifuge, Thermo Fisher Scientific, San Jose, California, USA). The digestates were decanted into clean centrifuge tubes and analyzed using an iCAP 7400 ICP-OES Duo equipped with a Charge Injection Device detector (Thermo Fisher Scientific, San Jose, California, USA). An aliquot of digested sample was aspirated from the centrifuge tube using a CETAC ASX-520 autosampler (Teledyne CETAC Technologies, Omaha, Nebraska, USA) and passed through a concentric tube nebulizer. The resulting aerosol was then swept through the plasma using argon as the carrier gas with a flow rate of 0.5 L/min and a nebulizer gas flow rate of 0.7 L/min. Macro- and micronutrients were quantified by monitoring the emission wavelengths (Em λ) reported in Table 2.Table 2 Macro- and micronutrients measured, and emission wavelengths (Em λ) used to quantify them in the miscanthus leaves (L) and stems (S), the total number and percentage detected (n = 150 for leaves and 162 for stems), the mean detected concentration ± standard deviation, and the mean method detection limit (MDL) ± standard deviation.Full size tableCharacterization of the water extractable organic matter (WEOM)The WEOM of the giant miscanthus leaves and stems was isolated by extracting the plant material with deionized water at room temperature6. The water extractions were performed by mixing ~0.2 g of dry, ground leaves and stems with 100 mL of deionized water in 125 mL pre-washed brown Nalgene bottles. All brown Nalgene bottles used for these extractions were pre-washed by soaking them for 24 hours in a 10% hydrochloric acid solution followed by 24 hours in a 10% sodium hydroxide solution, and a thorough rinse with deionized water. The bottles containing the extraction solution were shaken on an orbital shaker at 180 rpm for 24 hours. The extract was vacuum filtered using 0.45 µm glass fibre filters (GF/F, Whatman) into pre-washed 60 mL brown Nalgene bottles. The filtered water extracts containing the WEOM were stored in the dark in a refrigerator (4 °C) until analysis by UV-Visible and fluorescence spectroscopy. Samples were visually inspected just prior to analysis to ensure no colloids or precipitates had formed during storage. Samples that had become visually cloudy were re-filtered.On the day of analysis, the water extracts were removed from the refrigerator and allowed to warm up to room temperature. Chemical characteristics of the WEOM were assessed through the analysis of optical properties on an Aqualog spectrofluorometer (Horiba Scientific, New Jersey, USA) equipped with a 150 W continuous output Xenon arc lamp. Excitation-emission matrix (EEM) scans were acquired in a 1 cm quartz cuvette with excitation wavelengths (Ex λ) scanned using a double-grating monochrometer from 240 to 621 nm at 3 nm intervals. Emission wavelengths (Em λ) were scanned from 246 to 693 nm at 2 nm intervals and emission spectra were collected using a Charge Coupled Device (CCD) detector. All fluorescence spectra were acquired in sample over reference ratio mode to account for potential fluctuations and wavelength dependency of the excitation lamp output. Samples were corrected for the inner filter effect7 and each sample EEM underwent spectral subtraction with a deionized water blank to remove the effects due to Raman scattering. Rayleigh masking was applied to remove the signal intensities for both the first and second order Rayleigh lines. Instrument bias related to wavelength-dependent efficiencies of the specific instrument’s optical components (gratings, mirrors, etc.) was automatically corrected by the Aqualog software after each spectral acquisition. The fluorescence intensities were normalized to the area under the water Raman peak collected on each day of analysis and are expressed in Raman-normalized intensity units (RU). All sample EEM processing was performed with the Aqualog software (version 4.0.0.86).The optical data obtained from the EEM scans were used to calculate several indices representative of WEOM chemical composition (Table 3) including the absorbance at 254 nm (Abs254), the ratio of the absorbance at 254 to 365 nm (Abs254:365), the ratio of the absorbance at 280 to 465 nm (Abs280:465), the spectral slope ratio (SR), the fluorescence index (FI), the humification index (HIX), the biological index (BIX), and the freshness index (β:α). The SR was calculated as the ratio of two spectral slope regions of the absorbance spectra (275–295 and 350–400 nm)8. The FI was calculated as the ratio of the emission intensities at Em λ 470 and 520 nm, at an Ex λ of 370 nm9. The HIX was calculated by dividing the emission intensity in the 435–480 nm region by the sum of emission intensities in the 300–345 and 435–480 nm regions, at an Ex λ of 255 nm10. The BIX was calculated as the ratio of emission intensities at 380 and 430 nm, at an Ex λ of 310 nm11. The freshness index β:α was calculated as the emission intensity at 380 nm divided by the maximum emission intensity between 420 and 432 nm, at an Ex λ of 310 nm12. To further characterize the giant miscanthus WEOM, the fluorescence intensity at specific excitation-emission pairs was also identified. The fluorescence peaks identified here have previously been reported for surface water samples and water extracts13 and include peak A (Ex λ 260, Em λ 450), peak C (Ex λ 340, Em λ 440), peak M (Ex λ 300, Em λ 390), peak B (Ex λ 275, Em λ 310), and peak T (Ex λ 275, Em λ 340). A brief description of these optical indices is provided in Table 3.Table 3 Description of the optical indices calculated from the excitation-emission matrix (EEM) fluorescence scans and used to analyze the WEOM composition of giant miscanthus leaves and stems.Full size table More

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