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Overview of hydrologic and ecological mapping protocolMapping hydrologic and ecological alteration at the stream reach level followed a 7-step process that builds upon several previously published methods (Fig. 1). The steps include: (1) compiling a nationwide dataset of streamflow gauges from the US Geological Survey (USGS) and distinguishing reference and non-reference gages and associated records21,22,23, (2) assembling stream flow records and calculating hydrologic indices23, (3) quantifying hydrologic alteration for stream gages22, (4) developing models to predict hydrologic alteration from human disturbance variables24, (5) using models to extrapolate hydrologic alteration to ungauged stream reaches24, (6) developing empirical models of fish species richness responses to hydrologic alteration17, and (7) mapping fish richness responses to ungauged stream reaches based on modeled estimates of hydrologic alteration. Methodological details are provided in each of the publications cited above; however, an overview of the steps is provided here. We elaborate more fully on the detailed methodology starting at step 3, as this reflects more of the focus of the technical validation of the dataset (Fig. 1).Fig. 1Overview of the 7-step approach used to map hydrologic alteration and ecological consequences in stream reaches of the conterminous US.Full size imageStep 1 – Compiling a nationwide streamflow datasetWe assembled streamflow information for 7,088 USGS stream gages with at least 15 years of daily discharge data as of 2010. We only included gages with at least 15 years of complete annual records (i.e., those with More

Data management and workflowsWe adopt best-practice approaches for open and reproducible research planning, execution, reporting, and management throughout the project (see e.g.29,30,31,32). Specifically, we use community-approved standards for experimental design and data collection29, and clean and manage the data using a fully scripted and reproducible data workflow, with data and code deposited at open repositories (Fig. 2).Fig. 2The data collection and management workflow of the FunCaB project. Reproducibility throughout the research process is assured as follows: Experimental design and data collection was based on best-practice community methods and protocols, adapted for the projects’ needs. Measurements were digitalized and the raw data stored in the project Open Science Foundation (OSF) repository before the raw data were cleaned and managed through code-based data curation, with version control secured via GitHub. The clean data are stored at the OSF repository, and a time-stamped version of the code to retrieve and clean data is provided through Zenodo. This data paper describes and documents the data collection and workflow, and describes how to access and use clean data, raw data, and code.Full size imageResearch site selection and basic information, and general study setupSite selectionOur study is conducted across the twelve calcareous grassland experimental sites in the Vestland Climate Grid (VCG), in south-western Norway (Fig. 1a). The VCG sites were chosen to fit within a climate grid reflecting a fully factorial design encompassing the major bioclimatic variation in Norway. Potential sites were identified using a combination of topographic maps, geological maps (NGU) and interpolated maps of summer temperature and annual precipitation using the 1960–1990 climate normal (100 m resolution gridded data, met.no; see33 and references therein). The three temperature levels (alpine, sub-alpine, boreal) and four levels of precipitation in the climate grid (Fig. 1b) were selected to reflect a difference in mean growing season temperature of ca. 2 °C between three temperature levels (alpine = 6.5 °C, sub-alpine = 8.5 °C, boreal = 10.5 °C mean temperature of the four warmest months of the year) and a difference in mean annual precipitation of 700 mm between four precipitation levels (precipitation levels 1 – 4 representing 700 mm, 1400 mm, 2100 mm, and 2800 mm, respectively). Climate data for the site selection was based on 100-m resolution downscaled data using the 1960–1990 climate normal from met.no. The final sites were selected from approximately 200 potential sites visited and surveyed in the summer of 2008, with selection criteria set to ensure that other factors such as grazing regime and history, bedrock, vegetation type and structure, slope and exposure were kept as constant as possible among the selected sites34. Geographical distance between sites is on average 15 km and ranges from 175 km to 650 m.Study system and experimental area selection within sitesAt each site, we selected an experimental area of ca. 75 –200 m2, targeting a homogeneous and representative part of the target grassland vegetation at large at that site. The experimental areas were placed on southerly-facing slopes, avoiding depressions and concave areas in the landscape and other features such as big rocks or formations that may affect light conditions, hydrology and/or snowdrift. The target vegetation type was forb-rich semi-natural upland grassland vegetation34,35, within the plant sociological association Potentillo-Festucetum ovinae tending towards Potentillo-Poligonium vivipara in the alpine sites and Nardo-Agrostion tenuis in some lowland sites36. The most common vascular plants across sites, based on sum of covers, are the graminoids Agrostis capillaris, Festuca rubra, Avenella flexuosa, Anthoxanthum odoratum, and Nardus stricta and the forbs Leucantemum vulgare, Hypericum maculatum, Silene acaulis, Alchemilla alpina, and Lotus corniculatus. Common bryophytes are Pleurotium schreberi, Hylocomium splendens, Polythricum spp, Racomitrium lanuginosum, R. fasciculare, and Dicranum spp. All sites were moderately grazed prior to the study by sheep, cattle, goats, reindeer, deer, moose, and/or horses; and the experimental areas were fenced for the duration of the study to prevent animal and human disturbance of the experimental infrastructure. The fenced area was lightly mowed at the end of each growing season to mimic past grazing pressure and minimize fence effects. For further description of the sites, see34 and for access to and further description of site-level data, see35.Block and experimental plot setupWithin these study areas we established four blocks, with a distance between the blocks ranging from one up to (in rare cases) 50 meters. Blocks were selectively placed in homogenous grassland vegetation, avoiding rocks, depressions, and other features as described above. Each block accommodates eight 25 × 25 cm plots, with at least 25 cm between adjacent plots. If a plot contained more than 10% bare rock, shrubs, or other non-grassland features, they were rejected or moved slightly to avoid these features. The plots were permanently marked with four aluminium 10-cm long pipes in the soil in the outer corners of all the 25 × 25 cm treatment plots, ensuring the pipes to fit the corners of a standardized vegetation analysis frame (aluminium frame demarking a 25 × 25 cm inner area, with poles fixed in the corners that fit into the aluminium tubes used for plot demarcation in the field). The upslope left corner tube was marked with a colour-coded waterproof tape. Note that in 31 out of 48 cases (12 sites × 4 blocks), the blocks were located within larger experimental blocks in the VCG sites, and control plots and various block-level data are then shared with other experiments in these larger blocks. Linking keys are described in the FunCaB data dictionaries below (see Fig. 3 and data records iii-vii below). For some datasets, additional plots within blocks were needed. These are described as needed below.Fig. 3Data structure for the FunCaB functional group removal experiment and associated Vestland Climate Grid (VCG) and FUNDER project data. Within each of the three projects, boxes represent data tables. The FunCaB project data tables include biomass of functional groups removed and forb species-level biomass (datasets i, ii), soil temperature and moisture (datasets iii, iv) plant community composition and the associated taxon table (dataset v), seedling recruitment (dataset vi), ecosystem carbon fluxes (dataset vii) and reflectance (dataset viii). Names of individual data tables are given in the coloured title area, and a selection of the main variables available within tables in the internal lists. For full sets of variables for each FunCaB dataset, see Tables 3–9. The lines linking three of the boxes exemplify links using species as keys across tables, note that all bold variables are shared between several tables and can be used as keys to join them. Keys can also be used to link to/from data from other projects in the VCG (for general VCG project keys, see top right hatched outline box, for keys between the FunCaB and FUNDER projects see the bottom right hatched outline box (both including an example value for each variable on the right). The (other) datasets* boxes refer to extensive datasets on plant community composition, cover, biomass, fitness, and reproduction available from previous projects in the VCG27 and upcoming datasets in the FUNDER project.Full size imageBackground abiotic and biotic data from the Vestland Climate GridThe Vestland Climate Grid field sites were established in 2008, and from a series of research projects within the grid over the years we have collected a broad range of datasets on the climate and environment, soils, land-use and environment, vegetation, and ecosystems, along with basic descriptive data of the 12 sites, as described in34. All these datasets are available from the previous projects through the VCG OSF (Open Science Framework) repository35, and the results are presented in associated papers, see for example34,37,38,39,40,41,42,43,44,45. The overall data structure, and the most relevant datasets from the VCG for the FunCaB project is laid out in Fig. 3, and briefly described below. Code to download and link these data to the FunCaB experimental data and sites are provided in the FunCaB github repository28 (see R/download_VCG_data).A new research project, ‘FUNDER – Direct and indirect climate impacts on the biodiversity and FUNctioning of the UNDERground ecosystem’ funded by the Norwegian Research Council KLIMAFORSK programme (project number 315249, 2021 – 2025) will augment the FunCaB experiment with data on the belowground components of the plant-soil ecosystem, including roots, mesofauna, fungi and microbes. These upcoming data will all link with the FunCaB and VCG project based on the given experimental, site and organismal keys, as indicated in Fig. 3.VCG Basic site-level attributesBasic descriptive data on the 12 sites include latitude, longitude, elevation, geology, land-use, soils, and their position within the climate grid (precipitation and temperature levels). These data are described in34,40, provided in35, and can be downloaded using28 (see R/download_VCG_data). For convenience, the climate grid information is also provided in the biomass dataset (see below).VCG Site-level climate dataTemperature was measured continuously at each of the 12 VCG sites at four heights (2 m and 30 cm above ground, at ground level, and 5 cm below ground), soil moisture was measured continuously with two replicate loggers ca. 5 cm below ground, and precipitation was measured at each site during the snow-free season. For these measurements, we used Delta T GP1 loggers (Delta T devices, Cambridge, UK) equipped with two temperature probes, two SM200 moisture sensors which were later replaced as necessary with SM300 and SM150T loggers, and an ARG 100 tipping bucket (EML LTD, North Shields, UK) from 2009 onwards. UTL-3 version 3.0 temperature loggers (GEOTEST AG, Zollikofen, Switzerland) were used for measuring the 2 m and 30 cm temperatures. Soil moisture was measured as the mean of four measurements taken along each side of the turf, several times during the growing season using a Delta T HH2 version 2.3 Moisture Meter with the same probes as for the GP1 logger (SM200, SM150T). These data are described in34,40, provided in35, and can be downloaded using28 (see folder R/download_VCG_data).VCG Soil chemical and structural dataOver the years, various soil chemical variables have been measured at the block level within each of the 12 VCG sites, including soil pH (2009) and % Loss-On-Ignition (2009, 2013), and available N, as sum of N available as NH4-N and NO3-N (available N per deployment period, 2010 & 2012). Soil pH was measured after adding 50 ml distilled water to 25 g soil and mixing for two hours. Loss-on-ignition (LOI), was measured by weighing dry soil (105 °C for 24, one hour in a desiccator), and burnt soil (six hours at 550 °C, one hour in the desiccator) and calculating LOI as the (burnt soil mass/dry soil mass) × 100. NH4-N and NO3-N were measured using in-situ ion exchange resin bags (IERBs) were used to measure the amount of plant-available nutrients in the soil. These data are partially described in34,40, and the full data are provided in35.VCG Litter decomposition dataDecomposition has been assessed at each of the 12 VCG sites using local plant litter and the Tea Bag Index method (Keuskamp et al., 2013). Local litter (dead leaves detached from live plants) was collected at each site in 2013 or 2014, with the specific timing of the collections at each site tuned to ensure that litter was present, not covered by snow, and not decomposed. In practice, this necessitated litter collection after snowmelt in spring in many sites. The litter was washed, dried, and stored in dark, dry, cool conditions. In 2016, five replicate litter bags containing 1 g of graminoid litter were buried at each site, and collected at four points in time after burial (1, 2, 3 and 12 months). Harvested litter bags were cleaned (soil and roots removed), dried for 48 h at 60 °C and weighed. The Tea Bag Index method46 was used in 2014, 2015 and 2016 to measure decomposition at all sites of the climate grid. At each site, 10 replicates of each tea type were buried pair-wise, 8 cm below ground and with at least 10 cm between the tea bags. For a couple of sites, the number of replicate tea bag pairs was higher in 2015 (12 replicates at the site Gudmedalen and 16 replicates at Låvisdalen). After collection, adhering soil particles and roots were removed and the tea bags were dried (48 h at 60 °C) and weighed. These data are partially described in47, and the full data are provided in35 and can be downloaded using28 (see folder R/download_VCG_data).VCG Species-level cover, biomass, and performance dataA variety of plant species and community composition, cover, biomass, fitness, and reproductive data exists for the sites and blocks in the VCG from 2008 to 2021. These data are described in e.g34,37,38,41,43,44,45,48,49,50, and provided in35.VCG Site-level plant functional traitsIn 2016 and 2017, we measured 11 leaf functional traits that are related to potential physiological growth rates and environmental tolerance of plants, following the standardized protocols in Pérez-Harguindeguy et al.51: leaf area (LA, cm2), leaf thickness (LT, mm), leaf dry matter content (LDMC, g/g), specific leaf area (SLA, cm2/g), carbon (C, %), nitrogen (N, %), phosphorus (P, %), carbon nitrogen ratio (C:N), nitrogen phosphorus ratio (N:P), carbon13 isotope ratio (δ13C, ‰), and nitrogen15 isotope ratio (δ15N, ‰). Trait data are available at the site level for species making up at least 80% of the vegetation cover in the control plots at each of the 12 VCG sites. The plants were collected outside of the experimental plots and within a 50 m perimeter from the blocks, and we aimed to collect up to five individuals from each species in each site. To avoid repeated sampling from a single clone, we selected individuals that were visibly separated from other ramets of that species. The sampled plant individuals were labelled, put in plastic bags with moist paper towels, and stored in darkness at 4 °C until processing, which was done as soon as possible and always within 4 days. These data are described in52, provided in35, and can be downloaded using28 (see folder R/download_VCG_data).Experimental designThe functional group removal experiment was designed to examine the impact of aboveground interactions among the major plant functional groups – graminoids, forbs and bryophytes – on the performance and functioning of other components of the vegetation and ecosystem. The experiment consists of eight 25 × 25 cm plots per site and block, with a fully factorial combination of removals of three plant functional groups, with treatments randomized within each block. The general experimental design, with the different removal treatments detailed, are provided as an insert to the timeline in Fig. 1c. The functional groups are delineated and abbreviated in the various datasets as follows: G = graminoids (including grasses, sedges and rushes), F = forbs (including herbaceous forbs, pteridophytes, dwarf-shrubs, and small individuals of trees and shrubs), B = Bryophytes (including mosses, liverworts, and hornworts). Note that all species are also coded by their respective functional group into which they were classified in the FunCaB taxon table. The experimental treatments are coded by functional group removed so that FGB = bare-ground gaps with all plants removed, FB = only graminoids remaining, GB = only forbs remaining, GF = only bryophytes remaining, B = graminoids and forbs remaining, F = bryophytes and graminoids remaining, G = bryophytes and forbs remaining, and C = intact vegetation controls with no vegetation removed. In 2016, four extra control (XC) plots were marked per site for aboveground biomass harvest and ecosystem carbon flux measurements. This sampling regime gave a total of 384 plots in the core FunCaB experiment, plus the additional 48 controls in 2016.Functional group removals were done once in 2015 (at peak growing season due to late snowmelt), twice per year in 2016 and 2017 (after the spring growth and at peak growing season) and annually from 2018 to 2021 (at peak growing season) as regrowth had declined (see below) and biannual removals were no longer necessary. At each sampling, all above-ground biomass of the relevant plant functional group was removed from each plot as follows: for each plot, all the above-ground parts of the relevant functional group(s) were removed using scissors and tweezers to cut the plants at the ground layer (i.e., the soil-vegetation interface). Roots and other below-ground parts were not removed, and non-target plant functional groups and litter were left intact.Species identification, taxonomy, and floraAll vascular plant species were identified to the species level in the field, with nomenclature following Lid and Lid53. Exceptions are sterile specimens of species that are not possible to identify without reproductive parts, and where flowers are either too rare or individuals too short-lived for comparisons of the position of individuals within the plots over years to be used to ascertain identifications (For example, Alchemilla spp. excluding A. alpina, and the annual Euphrasia spp.). Species identifications were confirmed by comparing records over time as described below. All unidentified specimens are included and flagged in the dataset, as described in Data Records below. The full taxon names are provided in the taxon table on OSF (Fig. 3).Dataset collection methodsDatasets (i–ii): Biomass and functional group removalAs described above, functional group removals were done once in 2015 at peak growing season, and twice per year in 2016 and 2017 (after the spring growth; at peak growing season) and annually at peak growing season from 2018 to 2021. For each removal plot and occasion, a picture was taken of the plot pre-removal, the biomass to be removed was collected in separate pre-marked paper bags for each functional group (graminoids, forbs and bryophytes), and a picture was taken post-removal. The collected biomass was then dried at 60 °C for 48 hours and weighed to the nearest 0.01 g (Model LPG-1002, VWR). From the four extra control (XC) plots in 2016, total above-ground biomass as well as litter (defined as dead biomass detached from live plants, see28) was collected at peak growing season. From these plots, biomass was sorted into functional groups as described above, except the forb functional group, which was sorted into species. The graminoid and bryophyte functional groups, each forb species, and litter were individually dried and weighed as described above. The data is available as (i) a biomass dataset, consisting of the removed biomass per plot, date, removal treatment, and functional group for all treatment plots, and the total biomass per functional group plus litter for the extra control plots in 2016, and (ii) a species-level forb biomass dataset from the extra control plots in 2016 (Fig. 3, Table 1).Datasets (iii-iv) – Soil microclimateWe measured soil temperature 3–5 cm below the soil surface for each plot using iButton temperature sensors (DS1922L, Manufacturer reports temperature accuracy of ±0.5 °C, Maxim Integrated INC., San Jose, CA, USA). The data are reported with a resolution of 0.0625 at 140 min intervals from June 2015 to July 2016. We measured soil moisture as volumetric soil moisture; expressed as % water volume per soil volume ((m3 water /m3 soil) × 100). These measurements were done c. 3–5 times during the growing seasons from 2015–2019, usually in connection with the flux and vegetation measurements, by taking the average of four measurements, one at each side of each plot (SM300, Manufacturer reports accuracy ±2.5% vol over 0 to 50% vol and 0–60 °C, Delta-T Devices, Cambridge, UK). The data is available as (iii) temperature and (iv) volumetric soil moisture % per plot and time point (temperature) or date (moisture) (Fig. 3, Table 1).Dataset (v): Vascular plant community composition and vegetation structureWe recorded the full vascular plant species composition of all experimental plots in 2015 (pre treatment), and the control plots plus the extra control plots in 2016. In 2017, 2018, and 2019, we recorded the community composition in controls and in the functional groups that remained in the experimental plots according to the plot’s treatment. At each analysis, each plot was sub-divided into 25 5 × 5 cm subplots, using a subplot overlay. We first recorded all species of vascular plants in the central five subplots, (i.e., the central + shaped area of each plot, Fig. 1c) noting the subplot cover of each species present in each of the five subplots (1 – 25% = 1, 26 – 50% = 2, 51 – 75% = 3, >76% = 4). Additionally, we noted if the individual was fertile (records circled if buds, flowers, or fruits were present). The five subplots were recorded and numbered (1-5) by row, and from left to right, starting from the top up-slope subplot. For the entire 25 × 25 cm plot, any additional species not present in one of the central subplots were recorded and their fertility noted. We then visually estimated the percentage cover of each vascular plant species in the whole plot to the nearest 1% and measured vegetation height in mm at four points within the plot. Note that the total coverage in each plot can exceed 100% due to layering of the vegetation. The vascular plant vegetation data is available as percentage cover and fertility status (sterile or fertile) per species per subplot and plot per sampling date, and vegetation height in mm per plot per sampling date (Fig. 3, Table 1).Other variables that were measured were percentage cover of bryophytes, litter, bare ground, and rock (measured per plot and per subplot) and moss layer depth in mm (mean of 4 measurements/plot), date of analysis, recorder/scribe (if any), and free-text comments. These data are available as % cover, depth in mm, date (year.month.day) and text strings per subplot and /or plot per sampling date (Fig. 3, Table 1).Dataset (vi): Seedling recruitmentThe total number of forb seedlings that emerged in the plots was recorded in 2018 and 2019. At peak growing season in 2018 (round 1, July-August, depending on site), all dicotyledonous seedlings were marked with wooden toothpicks and their x and y coordinates in the plot (mm, recorded from the bottom left hand-corner of the plot, Fig. 1c) and tentative species identity noted. Toward the end of the growing season (round 2, August-September, depending on site), each plot was revisited, seedling survival established, and any further seedlings marked. Survival (recorded when a seedling was present in subsequent surveys; recorded as mortality if absent) and new seedling emergence were followed up in the same manner in 2019 (rounds 3 and 4, respectively). Species identification was (re)assessed at all censuses and corrected if needed as the seedlings grew and identification uncertainty decreased. New seedlings were differentiated from emergent clonal ramets by looking for cotyledons or signs of above- or below-ground ramet connections. These data are available as talleys of seedlings, each with a status (dead or alive) and species identity (or NA when not identifiable), per subplot and /or plot per sampling round (Fig. 3, Table 1).Dataset (vii): Ecosystem carbon flux data and flux calculationsCarbon flux measurementsEcosystem CO2 fluxes were measured to estimate net ecosystem exchange (NEE), ecosystem respiration (Reco) and gross primary production (GPP). The dataset covers the years 2015, 2016 and 2017, and individual plots have multiple measurements for ecosystem carbon flux per year as detailed below. At peak growing season in 2015, a median of 2 sets of paired carbon flux measurements were measured pre-removal for all plots, where a paired set consist of a light and a dark flux measurement of an individual plot. In 2016, a median of 8 sets of paired measurements were made for all control plots, and a median of 7 for the 4 extra controls (see experimental design above). In the data files, some additional measurements exist for other experiments in the VCG sites (a median of 7 paired sets of measurements for controls (TTC) and graminoid removal plots (RTC), see42 for a presentation of this experiment and35 for technical details). In 2017, a median of 5 paired sets of measurements were made for all treated plots in nine of the sites, excluding the second wettest precipitation level (sites Gudmedalen, Rambera, and Arhelleren). These measurements were made ca. 1 week after the first round of plant functional group removals in that season.At each sampling occasion, a clear chamber (25 × 25 × 40 cm) equipped with two fans for air circulation and connected to an infrared gas analyzer (Li-840; Manufacturer reports accuracy within 1.5% of the reading value; LI-COR Biosciences, Lincoln, NE, USA) was used to measure CO2 fluxes at all plots. To prevent cutting of roots and disruption of water flow in the plots by installing collars, we instead attached a windshield to the bottom of the chamber and weighed it down on the ground by a heavy chain to prevent wind-air mixing. At each sampling occasion we made paired measurements of fluxes under light and dark conditions, covering the chamber with a fitted light-excluding cover for the dark measurements.NEE was estimated from measurements of CO2 flux under ambient light and dark conditions: NEElight = GPP – Reco, NEEdark = (-) Reco. We define NEE such that negative values reflect CO2 uptake in the ecosystem, and positive values reflect CO2 release from the ecosystem to the atmosphere. For each measurement, CO2 concentration was recorded at 5 s intervals over a period of 90–120 s. NEE was calculated from the temporal change of CO2 concentration within the closed chamber according to the following formula:$$NEE=frac{delta C{O}_{2}}{delta t}times frac{PV}{Rtimes Atimes (T+273.15)}$$where (delta frac{C{O}_{2}}{delta t}) is the slope of the CO2 concentration against time (µmol mol−1 s−1), P is the atmospheric pressure (kPa), R is the gasconstante (8.314 kPa m3 K−1 mol−1), T is the air temperature inside the chamber (°C), V is the chamber volume (m3) and A is the surface area (m2).Light intensity was measured as photosynthetically active radiation (PAR, µmol m−2 s−1) using a quantum sensor (Li-190; Manufacturer reports absolute calibration accuracy of ±5%; LI-COR Biosciences, Lincoln, NE, USA) placed inside the chamber. Temperature inside the chamber was measured using an iButton temperature logger (DS1922L, Manufacturer reports temperature accuracy of ±0.5 °C, Maxim Integrated, San Jose, CA, USA). Volumetric soil moisture content (m3 water/m3 soil) × 100 was measured by calculating the average of four measurements with a soil moisture sensor (SM300, Manufacturer reports moisture accuracy of ±2.5%, Delta-T Devices, Cambridge, UK), taken at each side of a plot.Data management and calculationsData from the LiCOR data logger and iButton was downloaded in the field and stored. The information from the field data sheets (metadata of CO2 measurements and plot soil moisture) was manually entered into digital worksheets, manually proof-read and stored. Data from the data logger (PAR and CO2) and the iButton temperature logger were linked based on information from the field data sheets. All measurements were first visually evaluated for quality and only measurements that showed a consistent linear relationship between CO2 over a time for a period of at least 60 s were used for NEE calculations. A second inclusion criterion was that this relationship had R2 ≤ 0.2 or R2 ≥ 0.8 for NEE measurement in light conditions and R2 ≥ 0.8 for NEE dark measurements (Reco). Measurements of NEE in light conditions with R2 ≤ 0.2 ensures representation of measurements with equal rates for Reco and GPP. Third, paired measurements that were more than 2 h apart were excluded. These data are available as raw fluxes and as GPP and Reco per plot per measurement (Fig. 3, Table 1).Dataset (vi): ReflectanceReflectance measures of Normalized Difference Vegetation Index (NDVI) were taken for each plot during the 2019 (post functional group removal) and 2021 (pre and post removal) field seasons (July-August), using a Trimble Greenseeker Handheld Crop Sensor (Trimble Inc., Sunnydale, CA, USA). As the sensor measures an elliptical plane, two measures perpendicular to each other were taken for each subplot (25 × 25 cm plot), with the centre of each ellipse being the centre of the subplot. Care was taken to ensure that sampling quadrat frames were not within the sensor range when conducting measurements (see methods Dataset ii). Measures of NDVI were taken at 60 cm above the surface where possible. Height was measured perpendicular to the sampled ground surface. These data are available as reflectance per plot per sampling date (Fig. 3, Table 1). More

Species composition of vegetation in the WLFZIn this survey, a total of 44 species in 43 genera of 21 families of vascular plants were found and confirmed in the reservoir WLFZ of the Jinsha River basin, among which, 13 genera and 13 species of Compositae, 4 genera and 4 species of Gramineae, 3 genera and 3 species of Amaranthaceae, 2 genera and 2 species of Verbenaceae, Labiatae, Umbelliferae, Cruciferae and Convolvulaceae, 1 genus and 2 species of Polygonaceae, and the remaining 12 families were all single genera. Compositae had the highest number of species, followed by Gramineae and Amaranthaceae, accounting for 29.55%, 9.09% and 6.82% of the total number of species in this survey, respectively, which are the main dominant families in the region.According to the life type classification system of the Flora of China, the plants in the WLFZ of this survey can be classified into five life types: annual herbs, perennial herbs, annual or biennial herbs, annual or perennial herbs, and biennial herbs. The community is overwhelmingly dominated by annuals with a high proportion of 54.55%, followed by perennials with 34.09% and the rest of all life types with a total of 11.36%.The higher number of annual plants indicates that the environmental conditions in the WLFZ are harsher after inundation by water storage, and plants that can complete their entire life cycle in a short period of time after receding water are more likely to survive compared to plants that take a long time to complete their entire life cycle.The vegetation types in each study area of the WLFZ are shown in Table 3, among which 17 species, including S. subulatum, E. humifusa, C. bonariensis, V. officinalis, O. biennis, S. plebeia, U. fissa, B. juncea, S. orientalis, D. repens, A. lividus, T. mongolicum, G. parviflora, P. praeruptorum, P. hys-terophorus, D. stramonium and Ph. Nil, are newly discovered species in the reservoir WLFZ, which are rarely reported in other reservoir WLFZ studies so far. Among the study areas, the Longkou study area was the richest in vegetation types, with the most families, species and life types among all study areas, and the number of perennial herb species was comparable to that of annual herb species, while all other study areas were mainly dominated by annual herbs. The vegetation composition of the remaining study areas averaged 6–8 families and 11–12 species, except for the Ludila study area with no plants growing and the Liyuan study area with only 5 families and 5 species. In general, each study area was dominated by Compositae and Gramineae.Table 3 Vegetation composition in each study area.Full size tableVegetation area, coverage, and percentage of the WLFZAccording to the vegetation classification in the WLFZ of each study area (Fig. 5 and Table 4), the vegetation coverage of the study areas of the Liyuan, Ahai, Ludila and Guanyinyan reservoir WLFZ were all less than 5%. The study area of Ludila was completey devoid of vegetation in the WLFZ. The coverage in Liyuan was only 0.02%, with mostly individual herbaceous plants sporadically distributed on the upper boundary of the WLFZ. In Ahai, C. dactylon grow concentratly in patches at the top of the WLFZ together with some other sparsely growing vegetation, with a coverage of 1.47%. The vegetation coverage of Guanyinyan was 3.21%, mainly distributed in the upper part of the WLFZ and expanding towards the middle. In this area, 30.39% of the vegetation was X. sibiricum, growing in large tracts as low seedlings; 21.03% was A. sessilis growing in patches, 10.87% was C. dactylon growing mainly on the upper boundary of the WLFZ, and 37.71% was a mixture of plants growing in clusters with only a few of each.Figure 5The results of vegetation classification in the WLFZ of each study area. (a) Liyuan, (b) Ahai (c) Longkaikou, (d) Ludila, (e) Guanyinyan, (f) Xiluodu. Note: Non-Veg (Non-vegetation), Other-Veg (Other vegetation), C. Dac (Cynodon dactylon), A. Ses (Alternanthera sessilis), C. Bon (Conyza bonariensis), Ch. Amb (Chenopodium ambrosioides), C. Can (Conyza canadensis), D. Rep (Dichondra repens), H. Sib (Hydrocotyle sibthorpioides), V. Off (Verbena officinalis), X. Sib (Xanthium sibiricum). (Generated with eCognition Developer, and the URL is https://www.ecognition.com).Full size imageTable 4 Vegetation area, vegetation coverage and vegetation classification accuracy of WLFZ in each study area.Full size tableThe vegetation coverage of Longkaikou and Xiluodu WLFZ was more abundant, 46.47% and 55.81% respectively. In Longkaikou, vegetation mainly covered the middle and upper parts of the WLFZ. Of the vegetation, 66.38% was C. dactylon, 26.50% was A. sessilis, 2.35% was H. sibthorpioides, 1.68% was Ch. ambrosioides, and 3.09% was a variety of vegetation species, only a few of each, divided into Other-Veg class.Due to weather and equipment constraints, we were unable to photograph the upper and lower boundaries of the WLFZ in Xiluodu study area, but we still obtained the images of the main part of the WLFZ, which consisted mainly of 58.4% X. sibiricum, 28.04% C. dactylon, 10.59% S. viridis, and 2.97% other vegetation.The vegetation coverage in the WLFZ of different reservoirs of the Jinsha River basin varied significantly, but in terms of quantity, most of them were absolutely dominated by 1–4 species, which were distributed in patches and strips, and covered an area and proportion far more than the rest of the vegetation, while the rest of the vegetation was sparse in quantity each and was sporadically distributed. C. dactylon, A. sessilis, X. sibiricum, S. viridis, H. sibthorpioides, Ch. Ambrosioides were the main dominant and pioneer species for vegetation restoration in the reservoir WLFZ of the Jinsha River basin.Spatial distribution pattern of vegetation in fluctuating zoneSince no vegetation survived in the Ludila study area, and the vegetation in the Liyuan, Ahai and Guanyinyan study areas was sparse, with less than 5% coverage, and all of them were concentrated in the upper part of the WLFZs (Fig. 5), this paper mainly analyzed the spatial distribution pattern of vegetation in the Longkou and Xiluodu study areas, which had better vegetation coverage.Landscape patternCA is a basic index for landscape pattern study, and LPI reflects the proportion of the largest patch in the landscape type to the total landscape area, which is an expression of patch dominance. The SHAPE and PAFRAC describe the complexity of patch shape, the larger the SHAPE value indicates the more complex patch shape; the closer the PAFRAC value to 1 indicates the more regular patch shape. PROX reflects the degree of proximity of each landscape type, the larger its value indicates the higher degree of patch aggregation and the lower degree of fragmentation; ENN describes the degree of physical connection of the landscape types, the larger its value indicates the greater distance between patches and the greater degree of fragmentation.From the overall landscape level (Fig. 6), in the Longkaikou study area, CA and LPI showed that the areas of vegetation patches were large, less spatially fluctuating and uniform distribution, with obvious patch dominance, reflecting characteristics of patchy distribution; PROX and ENN showed that the vegetation patches were clustered and the landscape was well connected; SHAPE and PAFRAC showed that there was little variation in the shape complexity of vegetation patches in most areas of the WLFZ.Figure 6Spatial characteristics of vegetation landscape pattern index in the Longkaikou study area (Generated with ArcGIS 10.5 software, and the URL is: https://www.esri.com/en-us/home).Full size imageAt the level of landscape types (Table 5), the vegetation landscape types in the Longkou study area included C. dactylon, A. sessilis, H. sibthorpioides and other vegetation, among which, C. dactylon showed significant advantages in patch area, patch dominance, patch aggregation and connectivity; followed by A. sessilis and H. sibthorpioides, A. sessilis was significantly better than H. sibthorpioides in patch area, but in patch shape, H. sibthorpioides was more aggregated than A. sessilis and had better patch connectivity; Other-Veg showed significant weaknesses in patch area and aggregation; there were no significant differences among the landscape types in patch shape.Table 5 Landscape index of patch types in the Longkaikou study area.Full size tableThe spatial characteristics of the vegetation landscape pattern index in the Xiluodu study area were shown in Fig. 7. From the overall level of the landscape, the area of vegetation patches and the dominance of patches were spatially variable, the vegetation was well connected, with obvious characteristics of patchy distribution, and the shape of vegetation patches did not show obvious spatial characteristics.Figure 7Spatial characteristics of vegetation landscape pattern index in the Xiluodu study area (Generated with ArcGIS 10.5 software, and the URL is:https://www.esri.com/en-us/home).Full size imageFrom the level of landscape types (Table 6), the vegetation landscape types in Xiluodu study area included four categories: X. sibiricum, C. dactylon, S. viridis and Other-Veg type. Among them, X. sibiricum showed obvious advantages in patch area, patch dominance, patch aggregation and connectivity, followed by C. dactylon, both of which were significantly better than S. viridis and Other-Veg, and the differences in patch shape complexity among landscape types were small.Table 6 Landscape index of patch types in the Xiluodu study area.Full size tableDistribution characteristics along terrainAccording to the statistics (Fig. 8), the vegetation area share of Longkaikou study area in the upper, middle and lower elevation gradients of the WLFZ was 54.61%, 26.62% and 18.77%, respectively, indicating that the vegetation was mostly in the upper part of the WLFZ, with a coverage of 83.80%, while the vegetation in the lower part was the least, with a coverage of less than 1%. From the viewpoint of each vegetation species, in the upper part of the WLFZ, C. dactylon had the largest area, accounting for 66.9% of the total vegetation area, followed by A. sessilis, accounting for 25.9%, while H. sibthorpioides and Other-Veg only survived in the upper part, accounting for 2.3% and 4.9% each. From the distribution of each slope class, the vegetation of the WLFZ gradually decreased with the increase of slope, and the vegetation was mainly concentrated in the range of slope 35°, and the coverage of each vegetation decreased significantly when the slope exceeded 35°. In the aspect, the distribution of vegetation in the WLFZ did not show any obvious preference. The surface relief in the study area of Longkou was generally low, and C. dactylon was mainly distributed in the range of surface relief less than 0.84 m. When the surface relief is greater than 2.52 m, the vegetation coverage tends to be close to 0. The vegetation showed no obvious distribution preference in terms of surface roughness and topographic wetness index.Figure 8Changes in vegetation coverage with topographic factors in the Longkaikou study area (Drawn with Origin 2018_64Bit, and the URL is https://www.OriginLab.cn/).Full size imageThe spatial distribution of vegetation in the study area of Xiluodu was shown in Fig. 9. The maximum drop in water level at Xiluodu study area can reach 60 m, but only the half of the upper part of the subsidence zone with a drop of about 30 m was photographed. The coverage rate of C. dactylon was the largest in this elevation gradient, S. viridis was mainly distributed in the uppermost part of the zone, while X. strumarium was well covered in all elevation gradients. From the distribution of surface relief, the overall vegetation coverage decreases with the increase of surface relief, with X. strumarium and S. viridis mainly distributed in the area of 0–3.45 m, while both the coverage of C. dactylon and Other-Veg were not much different across the surface relief . The distribution of vegetation showed no obvious preference in terms of slope, aspect, surface roughness and topographic wetness index.Figure 9Changes in vegetation coverage with topographic factors in the Xiluodu study area (Drawn with Origin 2018_64Bit, and the URL is https://www.OriginLab.cn/).Full size imageInfluence of topographic factors on the spatial distribution pattern of vegetation in the WLFZAccording to the results of species distribution modeling, the number of samples in the study area of Longkaikou was 39,321, and the overall accuracy of the model was 88.2%. The terrain factors, in descending order of importance, were elevation  > slope  > surface relief  > surface roughness  > aspect  > topographic wetness index, with values of 0.681, 0.146, 0.091, 0.042, 0.033 and 0.007, respectively (Fig. 10). It can be seen that the vegetation distribution in the WLFZ was mainly influenced by elevation, followed by slope and surface relief, and is less influenced by surface roughness, aspect and topographic wetness index. This was consistent with the results of typical correlation analysis.Figure 10Ranking of important values of topographic factors in the Longkaikou study area (Drawn with Origin 2018_64Bit, and the URL is https://www.OriginLab.cn/).Full size imageA total of six pairs of typical variables were calculated in the Longkou study area, and standardized typical coefficients were used due to the inconsistency of each landscape pattern index as well as topographic factor units. According to the results of significance test (Table 7), the first four pairs of typical p-values were less than 0.05, indicating that the correlations reached a significant level, and their correlation coefficients were 0.565, 0.262, 0.142, and 0.034, among which the correlation coefficient of the first pair was the largest, so the first pair was selected for analysis. The topographic factors and landscape indices highly correlated with the first pair of typical variables were elevation, surface relief and CA and SHAPE, respectively. According to Tables 8 and 9, their mechanism of action was that the greater the elevation, the smaller the surface relief, resulting in a larger patch size and more complex shape of the vegetation, and therefore a more frequent exchange of energy with the outside world and a greater ability to survive.Table 7 Significance test of typical correlation coefficient in the Longkaikou study area.Full size tableTable 8 Standardized canonical correlation coefficients of terrain factors in the Longkaikou study area.Full size tableTable 9 Standardized typical correlation coefficients of landscape pattern in the Longkaikou study area.Full size tableThe number of samples in the study area of Xiluodu was 41,010, and the overall accuracy of the model was 61.4%. The terrain factors, in descending order of importance, were elevation  > surface relief  > ground roughness  > aspect  > slope  > terrain moisture index, with values of 0.395, 0.209, 0.157, 0.123, 0.073, and 0.043, respectively (Fig. 11). It can be seen that the vegetation distribution in the WLFZ was most influenced by the elevation, followed by the surface relief.According to the typical correlation analysis, six pairs of typical variables were calculated for the Xiluodu study area, of which the first four pairs had typical P values less than 0.05 (Table 10), indicating that the correlation reached a significant level, and their correlation coefficients were 0.299, 0.208, 0.102, and 0.033, and the first pair was the largest, so the first pair was selected for analysis.The topographic factors and landscape indices with high correlation with the first pair of typical variables were elevation,surface relief and CA, PAFRAC, respectively, and according to Tables 11 and 12, their mechanism of action was that the greater the elevation, the greater the surface relief, leading to a smaller patch area and simpler shape of the vegetation.Figure 11Ranking of important values of topographic factors in the Xiluodu study area (Drawn with Origin 2018_64Bit, and the URL is https://www.OriginLab.cn/).Full size imageTable 10 Significance test of typical correlation coefficient in the Xiluodu study area.Full size tableTable 11 Standardized canonical correlation coefficients of terrain factors in the Xiluodu study area.Full size tableTable 12 Standardized typical correlation coefficients of landscape pattern in the Xiluodu study area.Full size tableLimiting factors of vegetation restoration in WLFZPreliminary studies showed that after long-term water level fluctuations in the cascade reservoirs, most of the vegetation in the WLFZs of the cascade reservoirs in the Jinsha River basin could be restored to different degrees, however, the restored species types were relatively simple, all of them were herbaceous plants, and mainly annual herbaceous plants. The restoration of the WLFZs of different reservoirs varied significantly, with vegetation coverage of more than 46% and 27 species types in the better restored areas, such as the Longkou study area, while the vegetation coverage of the less restored areas was usually less than 5% and 5–12 species types, and some areas even had no grass, such as the Ludila study area. According to the statistics (Fig. 12), the habitats in the study area of different reservoirs in the Jinsha River basin were significantly heterogeneous, with significant differences in climate, soil conditions, topography, and water level drop, etc. Because of the inconsistent range of values and units of different environmental factors, comparative analysis was performed by normalization, as shown in Fig. 12, vegetation cover was significantly correlated with the average soil Ph and the average thickness of the subsurface 30 cm soil layer, and the two study areas with average soil Ph greater than 8, Pear Garden and Rudyra, were almost completely bare. These two study areas were almost dominated by sand and gravel, with thin soils averaging  8 and soil thickness  More

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Mixing fire occurrence with wildfire activity is problematic also when trying to draw policy conclusions. Fang et al.1 examined the temporal pattern of fire numbers between 2005-18 and concluded that the application of a fire suppression policy after 1987 has contributed to decreases in fire occurrences after 2007. However, fire suppression is an effort to mitigate the results of a fire once it has started10. Consequently, fire suppression strictly affects the burned area, and not fire occurrence. Other aspects associated with fire planning, like awareness campaigns or fire bans, may act on fire occurrence. However, any relationship between fire occurrence and fire suppression will necessarily be artefactual because the latter does not affect the former.We acknowledge that part of the discrepancy with Fang et al.1 may lie in the different scales used in these analyses. However, fire activity is a term that currently lacks a rigorous definition and should be used with caution. Fire occurrence depends primarily on the number of ignitions (along with other factors affecting fire detection such as climate, topography or vegetation), which, in turn, results from human activity1 and, in some areas, lightning11. Using fire occurrence as an indicator for fire activity is particularly problematic when comparing multiple biomes that show marked differences in fire regime, as we demonstrate here. Additionally, ENSO and fire suppression may both affect burned area, but there is currently no mechanism that can explain a mechanistic link between either of these processes and the number of fire events. Consequently, fire occurrence should not be used as a sole metric of fire activity.We additionally note that burned area is not necessarily a reliable metric of fire impacts on ecosystems and society. Significant variation in severity and intensity may occur within a fire perimeter12. Additionally, damage to people and property are not captured by this metric13. While we caution against the use of a single metric to evaluate fire activity, we hope to have demonstrated that using fire occurrence alone is particularly problematic, and that the picture it paints is rather unrealistic. More