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1. Species Abundance Counts of Benthic Diatoms in Response to Phosphorus Fertilization: Data from the Upper Kuparuk River Experiment (UKRE), Alaska, 1997-2022

   https://doi.org/10.18739/A2PG1HQ44  

   Sahlmann, Lindsey (January 2024, NSF Arctic Data Center)

   This dataset contains species abundance data of epilithic diatoms collected from the Upper Kuparuk River, Alaska (USA), spanning from 1997 to 2022. The dataset is part of a long-term study aimed at understanding the ecological impacts of phosphorus enrichment in Arctic freshwater ecosystems. Samples were collected annually from both phosphorus-fertilized and reference reaches of the river to assess the influence of nutrient enrichment on diatom community composition. The dataset includes detailed records of diatom species identified and their relative abundances in the phosphorus reach only. Analyses of this dataset using permutational multivariate analysis of variance (PERMANOVA) and Redundancy Analysis (RDA) revealed significant shifts in diatom communities attributable to phosphorus enrichment. Indicator Species Analysis identified key diatom species that are reliable indicators of phosphorus-enriched conditions. This dataset provides valuable insights into the long-term responses of diatom communities to nutrient enrichment and the ecological interactions with mosses that colonized the fertilized reach. It serves as an important resource for researchers studying nutrient dynamics, ecological monitoring, and the impacts of climate change on Arctic freshwater systems. 

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2. Robust species-optima estimates from non-uniformly sampled environmental gradients

   https://doi.org/10.1007/s10933-025-00354-2  

   Solomon, Kelsey J; Stevenson, R Jan; Surratt, Donatto; Whelan, Kevin_R T; Tobias, Franco_A C; Johnson, Katherine M; Gaiser, Evelyn E (June 2025, Journal of Paleolimnology)

   Abstract Abundance-weighted averaging is a simple and common method for estimating taxon preferences (optima) for phosphorus (P) and other environmental drivers of freshwater-ecosystem health. These optima can then be used to develop transfer functions to infer current and/or past environmental conditions of aquatic ecosystems in water-quality assessments and/or paleolimnological studies. However, estimates of species’ environmental preferences are influenced by the sample distribution and length of environmental gradients, which can differ between datasets used to develop and apply a transfer function. Here, we introduce a subsampling method to ensure a uniform and comparable distribution of samples along a P gradient in two similar ecosystems: the Everglades Protection Areas (EPA) and Big Cypress National Preserve (BICY) in South Florida, USA. Diatom optima were estimated for both wetlands using weighted averaging of untransformed and log-transformed periphyton mat total phosphorus (mat TP) values from the original datasets. We compared these estimates to those derived from random subsets of the original datasets. These subsets, referred to as “SUD” datasets, were created to ensure a uniform distribution of mat TP values along the gradient (both untransformed and log-transformed). We found that diatom assemblages in BICY and EPA were similar, dominated by taxa indicating oligotrophic conditions, and strongly influenced by P gradients. However, the original BICY datasets contained more samples with elevated mat TP concentrations than the EPA datasets, introducing a mathematical bias and resulting in a higher abundance of taxa with high mat TP optima in BICY. The weighted averaged mat TP optima of BICY and EPA taxa were positively correlated across all four dataset types, with taxa optima of SUD datasets exhibiting higher correlations than in the original datasets. Equalizing the mat TP sample distribution in the two datasets confirmed consistent mat TP estimates for diatom taxa between the two wetland complexes and improved transfer-function performance. Our findings suggest that diatom environmental preferences may be more reliable across regional scales than previously suggested and support the application of models developed in one region to another nearby region if environmental gradient lengths are equalized and data distribution along gradients is uniform. 

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3. Environmental and periphyton composition data from Biscayne Bay Coastal Wetlands, Florida, USA, July 2022 - November 2022

   https://doi.org/10.6073/pasta/2c431dcc812c569d4fe3fc15e574d829  

   Hormiga, Samantha; Gaiser, Evelyn E (January 2024, Environmental Data Initiative)

   Environmental and periphyton data were collected from transects in the Biscayne Bay Coastal Wetlands (BBCW) during the wet and dry seasons of 2022 to investigate the rate of carbonate sediment production by periphyton. Environmental data include surface water metrics (pH, salinity, conductivity, and water depth) and soil depths. Periphyton data include nutrient, production, and diatom species composition in samples collected from artificial substrates (periphytometers) placed in the field. Data collection for this project is complete, although the South Florida Management District continues to monitor these transects for a larger ongoing BBCW project. 

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4. Long-term patterns and trends in water column biogeochemistry in a changing environment

   https://doi.org/10.1016/j.ecss.2024.108896  

   Julian, Paul; Fourqurean, James W; Davis, Stephen E; Surratt, Donatto; Gaiser, Evelyn E; Kominoski, John S; Troxler, Tiffany G; Boyer, Joseph N; Thomas, Serge; Briceño, Henry O; et al (October 2024, Estuarine, Coastal and Shelf Science)

   Water column nutrient concentrations and autotrophy in oligotrophic ecosystems are sensitive to eutrophication and other long-term environmental changes and disturbances. Disturbance can be defined as an event or process that changes the structure and response of an ecosystem to other environmental drivers. The role disturbance plays in regulating ecosystem functions is challenging because the effect of the disturbance can vary in magnitude, duration, and extent spatially and temporally. We measured changes in total nitrogen (TN), dissolved inorganic nutrient (DIN), total phosphorus (TP), soluble reactive phosphorus (SRP), total organic carbon (TOC), and chlorophyll-a (Chl-a) concentrations throughout the Everglades, Florida Bay, and the Florida Keys. This region has been subjected to a variety of natural and anthropogenic disturbances including tropical storms, fires, eutrophication, and rapid increases in water levels from sea-level rise and freshwater restoration. We hypothesized that the rate of change in water quality would be greatest in the coastal ecotone where disturbance frequencies and marine P concentrations are highest, and in freshwater marshes closest to hydrologic changes from restoration. We applied trend analyses on multi-decadal data (1996–2019) collected from 461 locations distributed from inland freshwater Everglades (ridge and slough) to outer marine reefs along the Florida Keys, USA. Total Organic Carbon decreased throughout the study area and was the only parameter with a systematic trend throughout the study area. All other parameters had spatially heterogeneous patterns in long-term trends. Results indicate more variability across a large spatial and temporal extent associated with changes in biogeochemical indicators and water quality conditions. Chemical and biological changes in oligotrophic ecosystems are important indicators of environmental change, and our regional ridge-to-reef assessment revealed ecosystem-specific responses to both long-term environmental changes and disturbance legacies. 

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5. Data and code from: Decoupled carbon assimilation and growth responses to aridity in temperate deciduous oaks

   https://doi.org/10.5061/dryad.m63xsj4h4  

   Rao, Mukund Palat; Pacheco-Solana, Arturo; Li, Rong; Oryan, Bar; Jensen, Johanna E; Rodriguez-Caton, Milagros; Klinek, Lily; Pierrat, Zoe; Ruehr, Sophie; Oelkers, Rose; et al (January 2026, Dryad)

   Abstract: The magnitude of the terrestrial carbon sink remains a key uncertainty in future climate projections, in part due to poorly understood links between carbon uptake and its allocation to woody biomass in vegetation. Here, we show that photosynthesis and above-ground growth occur asynchronously across diel to seasonal scales in eight North American oak species. Across 137 tree-ring sites, current-year annual growth was insensitive to climate variability after mid-summer, despite 26-36% of annual gross primary productivity (GPP) occurring during this period. Hourly GPP flux and growth measurements at four sites spanning seven site-years further demonstrate that wood formation ceases earlier than photosynthesis and is restricted to periods of low atmospheric aridity and temperature. This photosynthesis-growth decoupling intensifies with inter-annual variability in vapour pressure deficit (r=0.86, p<0.05), suggesting that by assuming tight coupling between photosynthesis and woody biomass, current earth system models may overestimate long-term carbon sequestration in forests. TechnicalInfo: # Data and code from: Decoupled carbon assimilation and growth responses to aridity in temperate deciduous oaks DOI: 10.5061/dryad.m63xsj4h4 Last date updated, May 22 2026, by Rao MP. The dataset (r_markdown.zip) contains tree growth, carbon flux, and environmental data for different oak sites across North America This includes 4 high-resolution monitoring sites in the USA, which include (See Supplementary Figure 1) 1. Morton Arboretum, IL, USA 41.82°N, 88.07°W * Point dendrometers * Phenocams * SIF derived GPP data (computed from Turner et al 2021) * Environmental data 2. Lamont Sanctuary, NY, USA 41°N, 73.90°W * Point dendrometers * Phenocam - [https://phenocam.nau.edu/webcam/sites/ldeocam/](https://phenocam.nau.edu/webcam/sites/ldeocam/) * SIF derived GPP data (computed from Turner et al 2021) * Xylogenesis/Wood Anatomy data * Leaf level chlorophyll fluorescence data using a Jr. Pam instrument * Environmental data 3. Pace Forest, VA, USA 78°N, 33°W * Point dendrometers * Phenocam * In-situ eddy covariance and environmental data 4. Tonzi Ranch, CA, USA 38.43°N, 120.96°W * Point dendrometers * Phenocam - [https://phenocam.nau.edu/webcam/sites/tonzi/](https://phenocam.nau.edu/webcam/sites/tonzi/) * In-situ eddy covariance and environmental data - [https://ameriflux.lbl.gov/sites/siteinfo/US-Ton](https://ameriflux.lbl.gov/sites/siteinfo/US-Ton) Also included are raw (cross-dated) tree ring measurements from 137 oak sites across North America (134 - USA & 3 - Canada) ## Sharing/Access information You may contact the lead author Mukund Palat Rao [mukund@ldeo.columbia.edu](mailto:mukund@ldeo.columbia.edu) or [mukund24rao@gmail.com](mailto:mukund24rao@gmail.com) for assistance in interpreting and using the data. Please also cite the original publication if these data or codes are used. Rao MP et al (2026), Decoupled carbon assimilation and growth responses to aridity in temperate deciduous oaks, Science Advances, DOI: 10.1126/sciadv.ady7139 Links to other publicly accessible locations of the data: The data are available directly through the manuscript ## Code/Software Files in this folder can be read using R (.Rmd), and included code can be used to read these data and replicated all analysis included in the papers. Files 1-4 read in dendrometer, PhenoCam, flux, or remote sensing, and environmental data for the 4 high-resolution monitoring sites to compare the phenology and environmental sensitivity of growth vs gross primary productivity (GPP) 1. IL_Morton_Dendro-GPP-Pheno_20260126.Rmd 2. CA_Tonzi_Dendro-GPP-Pheno_20260208.Rmd 3. VA_Pace_Dendro-GPP-Pheno_20260218.Rmd 4. NY_Lamont_Dendro-GPP-Pheno_20260222.Rmd Files 5-6 are specific to Lamont-NY for leaf-level chlorophyll fluorescence data (using a Jr. PAM) and wood anatomy data 5. jrPAM_lamont_20240522.Rmd 6. NY_woodanatomy_20240401.Rmd File 7. Evaluates environmental data at all 4 sites and evaluates the environmental driver of the 'offset' variable 7. env_cv_vs_growth_gpp_20260223.Rmd File 8. Evaluates the environmental drivers of growth at 137 tree-ring sites 8. dendro_sensitivity_20240506.Rmd * climcorr_20260221.R is an embedded dependent function for the climate-growth response analysis File 9. Evaluates the phenology of Gross Primary Productivity (GPP) across all 137 tree-ring sites based on the Turner et al (2021) data set using solar-induced chlorophyll fluorescence (SIF). [https://doi.org/10.3334/ORNLDAAC/1875](https://doi.org/10.3334/ORNLDAAC/1875) Note that the code directly reads in downloaded data just for the 137 sites; however, commented code is included that would read in data from the original NETCDF files In this case, the user needs to download the Turner et al. data by themselves first. 9. turner_gpp_sites_20241104.R These files have been tested on R version 4.4.2. In each file, the working directory will need to be changed to your working directory in the first section. All required packages and dependencies will be auto-installed (if unavailable locally) # Knit HTML versions of each file are also included These files are HTML versions that show code and embedded figures in a web browser without needed to run the code 1. IL_Morton_Dendro-GPP-Pheno_20260126.html (from IL_Morton_Dendro-GPP-Pheno_20260126.Rmd) 2. NY_Lamont_Dendro-GPP-Pheno_20260222.html (from NY_Lamont_Dendro-GPP-Pheno_20260222.Rmd) 3. VA_Pace_Dendro-GPP-Pheno_20260218.html (from VA_Pace_Dendro-GPP-Pheno_20260218.Rmd) 4. CA_Tonzi_Dendro-GPP-Pheno_20260208.html (from CA_Tonzi_Dendro-GPP-Pheno_20260208.Rmd) 5. jrPAM_lamont_20240522.html (from jrPAM_lamont_20240522.Rmd) 6. NY_woodanatomy_20240401.html (from NY_woodanatomy_20240401.Rmd) ## Data This includes files included inside the data folder that are read into R 1. CRU * CRU Ts v 4.06 downloaded on Jan 18, 2023 * These data are used to compute the climate sensitivity of tree ring data in dendro_sensitivity_20240506.Rmd * Instructions to download data are provided inside the readme.text within the Data/CRU/ts.4.06/ folder. 2. tree-rings * Includes tree ring data in Tucson Format organised by US State (two letter code) and Canada (CAN). More information on each site is provided in Supplementary Table 1 * ITRDB_readme.txt - includes information on codes used to download tree ring data from the International Tree Ring Data Bank (ITRDB). * See Brewer et al 2011 for more information on the Tucson Format [https://bioone.org/journals/tree-ring-research/volume-67/issue-2/2010-12.1/Tricycle--A-Universal-Conversion-Tool-For-Digital-Tree-Ring/10.3959/2010-12.1.full](https://bioone.org/journals/tree-ring-research/volume-67/issue-2/2010-12.1/Tricycle--A-Universal-Conversion-Tool-For-Digital-Tree-Ring/10.3959/2010-12.1.full) 3. US_CA_Tonzi * phenocam - data downloaded from Phenocam Website, see [https://phenocam.nau.edu/webcam/tools/summary_file_format/](https://phenocam.nau.edu/webcam/tools/summary_file_format/) for descriptions of each column * dendrometers - Dendrometer data on site - US_CA_TON_20240125_clean.csv includes the following columns - time (local time in California, UTC-8) - year - tree (codes from TON1 to TON5) - species (4-letter species code, QUDO - Quercus douglasii), - val (change in tree radius in micrometers based on point dendrometer) - gr (tree radial growth in micrometers based on the zero-growth model) - battery (dendrometer battery voltage) * flux - data downloaded from the Ameriflux website, see README_AmeriFlux_BASE.txt for information on headers and data interpretation Information on column names is also available here [https://fluxnet.org/data/aboutdata/data-variables/](https://fluxnet.org/data/aboutdata/data-variables/) Note that we cannot provide these data directly, as they use to CC-BY-4.0 (By ATTRIBUTION). Instructions on how to download Ameriflux data are provided in data/US_CA_Tonzi/flux/20251124/AMF_US-Ton_BASE-BADM_25-5 in the readme.txt. Once data are downloaded, we use the following columns File name AMF_US-Ton_BASE_HH_25-5.csv Columns that are used - TIMESTAMP_START - start time of measurement - TIMESTAMP_END - end time of measurement - GPP_PI_F_1_1_1 - Gross Primary Productivity based on nightime method for the tall tower μmol/(m^2.s) - GPP_PI_F_1_2_1 - Gross Primary Productivity based on nightime method for the tall tower μmol/(m^2.s) - TA_PI_F_1_1_1 - Photosynthetic Photon Flux Density mol photons/(m2.s) - SWC_PI_F_1_2_1 SWC_PI_F_2_2_1, SWC_PI_F_3_2_1 SWC_PI_F_4_2_1, WC_PI_F_5_1_1, SWC_PI_F_6_3_1 SWC_PI_F_7_3_1 - Soil Water Content (m3 water/m3 soil), the numbers indicate horizontal, vertical, and repetitions H_V_R (Horizontal_Vertical_Replicate), ere each measurement is in a different horizontal location 1-7. 1. US_IL_Morton * sif - data on solar induced chlorophyll fluorescence (SIF) and SIF-Derived GPP around Morton Arboretum, as calculated by turner_gpp_sites_20241104.R Morton_Arborutum_Turner_SIF_GPP_2019_2021.csv includes the following columns - Year - Month (month from 1-12) - Day (day of month) - SIF (mW m−2 sr−1 nm−1) - GPP (in gC/(m2.day)) * phenocam - Phenological Camera information OakPhenology2019-2022.csv includes the following columns - Plot (4 plots for different species) - Datetime (Local datetime in Ilinois, USA, Central Time Zone UTC-6) - Date (date in yyyy-mm-dd) - Year (calendar year) - Day (Day of Year) - Jday (Julian Day) - Hour (Hour of Day) - Minute - Red (reflectance in red band in an 8-bit colour system from 0-255 for the region of interest - ROI) - Green (reflectance in green band in an 8-bit colour system from 0-255 for the region of interest - ROI) - Blue (reflectance in blue band in an 8-bit colour system from 0-255 for the region of interest - ROI) - gcc (Green Chromatic Coordinate - Green/(Red+Green+Blue) - ROI (Region of Interest, 3 per plot) * environmental - micrometeorological data at Morton Arboretum Please see the README weather_processe.docx within the same folder for description of the column names * dendrometers - Contains dendrometer data organised by Plot and Tree in micrometers in the file US_IL_MORTON_202103-202302_clean.csv Columns are defined as: - ts (Local datetime in Ilinois, USA, Central Time Zone UTC-6) - Series (unique code for 20 different trees monitored using dendrometers with codes for species, plot, and tree number within the plot) - Year (year) - Plot (code for Plot ID, 4 plots "QUAL-E" "QUBI-E" "QUBI-W" "QUPA-W") - Tree (tree number within plot) - val (change in tree radius in micrometers based on point dendrometer) - val_growth (tree radial growth in micrometers based on the zero-growth model) 1. US_NY_Lamont * phenocam - data downloaded from Phenocam Website, see [https://phenocam.nau.edu/webcam/tools/summary_file_format/](https://phenocam.nau.edu/webcam/tools/summary_file_format/) for descriptions of each column * anatomy - wood anatomical data at Lamont Sanctuary Forest, NY in 2021 based on xylogenesis sampling - For Column definitions, please see the embedded ROXAS_column_dictionary.xlsx file - This includes definitions for each column - Columns that are used in analyses are - RCTA - Relative Conductive Area Over Time - DOY - Day of year * dendrometers - dendrometer data on Lamond-Doherty Earth Observatory campus (site name Lamont Sanctuary-NY) File name hourly_dendro_env_20240401.csv with columns - variable ("dendro" for dendrometer, "airT" - for air temperature in degree C,"soilT" - for soil temperature in deg. C, "VPD" - for Vapour Pressure Deficit in kPa "VWC" - for volumetric water content at -10 cm) - tree (tree code for 8 trees with dendrometers, "oak1", "oak2", "oak3", "oak4", "redoak1", "redoak2", "redoak3", "whiteoak") - year (calendar year) - date (calendar date) - time (Local datetime in New York, USA, Central Time Zone UTC-4) - val (change in tree radius in micrometers based on point dendrometer) - battV (battery voltage 4 * 1.5V batteries, not used in analysis) * environmental - micrometeorological data on a campus Five different files with environmental data spanning different periods 1. 06-02000(06-02000)-Configuration 1-1710495780.0390148.csv 2. 06-02000(06-02000)-Configuration 2-1710495780.0390148.csv 3. 06-02000(06-02000)-Configuration 3-1710495780.0390148.csv 4. 06-02000(06-02000)-Configuration 4-1710495780.0390148.csv 5. 06-02000(06-02000)-Configuration 5-1710495780.0390148.csv Column definitions are "TA" = "degree_C Air Temperature", "TS" = "degree_C Soil Temperature", "VPD" = "kPa VPD - Vapour Pressure Deficit", "VP" = "kPa Vapor Pressure", "PA" = "kPa Atmospheric Pressure", "SWC" = "m3/m3 Water Content" * JrPAM - leave-level chlorophyll fluorescence data collected from a Jr. PAM on 3 trees between October-November 2021 Please see the Walz Jr. Pam handbook for a detailed explaination of all columns Available at [https://www.walz.com/products/junior-pam/](https://www.walz.com/products/junior-pam/) Column names are "F" = "1:F", "Fmp" = "1:Fm'", "PAR" = "1:PAR", Photosynthetically Active Radiation, Photosynthetic Photon Flux Density (PPFD), μmol/m2/s "Temp" = "1:Temp", Leaf temperature in degrees C. "YII" = "1:Y (II)", Effective quantum yield. Measures the proportion of light absorbed by PSII that is used for photochemistry in the light. "ETR" = "1:ETR", electron Transport Rate. Estimates the rate of electrons transported through the photosynthetic chain "FoP" = "1:Fo'", "ETRmf"= "1:ETR-F.", "qP" = "1:qP", Photochemical/Non-photochemical Quenching Coefficients. "qN" = "1:qN", Photochemical/Non-photochemical Quenching Coefficients. "qL" = "1:qL", Photochemical/Non-photochemical Quenching Coefficients. "NPQ" = "1:NPQ", Non-Photochemical Quenching. "Y_NO" = "1:Y (NO)", Quantum yield of non-regulated energy dissipation (energy lost as heat and fluorescence without being safely regulated). "Y_NPQ"= "1:Y (NPQ)", Quantum yield of regulated, light-induced energy dissipation (heat loss). "Fo" = "1:Fo", Minimal fluorescence. The baseline fluorescence level emitted when all Photosystem II (PSII) reaction centers are fully "open" (oxidized) after dark-adaptation. "Fm" = "1:Fm", Maximum fluorescence. The peak signal when a saturating flash of light temporarily closes all PSII reaction centers. "FvFm" = "1:Fv/Fm" Maximum quantum yield of Photosystem II * sif - data on solar induced chlorophyll fluorescence (SIF) and SIF-Derived GPP around Morton Arboretum, as calculated by turner_gpp_sites_20241104.R Turner_SIF_GPP_2019_2021.csv includes the following columns - Year - Month (month from 1-12) - Day (day of month) - SIF (mW m−2 sr−1 nm−1) - GPP (in gC/(m2.day)) 1. US_VA_Pace * phenocam - Green Chromatic Coordinate is calculated by Fluospec2 spectrometer on site as GCC = (green/(red+green+blue)) two files, Pace_GCC_2023_nostablefilter.txt & Pace_GCC_2023.txt Columns are - t_10min (local standard tiem in Pace Forest, Virgina, USA) - GCC_10min (green chromatic coordinate) * dendrometers - Dendrometer data on site US_VA_Pace_20251121.csv - time (Local time) - Tree (Tree code from Pace 1 to 5) - Species (which species, Quercus falcata, Pinus virginiana, Quercus alba, Quercus rubra) - Genus (Pine or oak) - val (change in tree radius in micrometers based on point dendrometer) * flux - data downloaded from Ameriflux website, see README_AmeriFlux_BASE.txt for information on headers and data interpretation Information on column names is also available here [https://fluxnet.org/data/aboutdata/data-variables/](https://fluxnet.org/data/aboutdata/data-variables/) File name FLX_US-Pac_FLUXNET_FULLSET_HH_2023_beta-5.csv Columns that are used - TIMESTAMP_START - start time of measurement - TIMESTAMP_END - end time of measurement - GPP_NT_VUT_REF - Gross Primary Productivity based on nightime method, μmol/(m^2.s) - GPP_DT_VUT_REF - Gross Primary Productivity based on daytime method, μmol/(m^2.s) - NEE_VUT_REF - Net Primary Productivity based on daytime method, μmol/(m^2.s) - PPFD_IN - Photosynthetic Photon Flux Density mol photons/(m2.s) 1. USTreeAtlas-main * Shapefiles for major tree species in the United States, see embedded README.md for more information data used for this study include shapefiles for study species - included in the shp folder. We use the following subfolders, querdoug, querrubr, queralba, querpalu, quercocc, querfalc, querprin for Quercus douglasii, Q. rubra, Q. alba, Q. palustris, Q. falcata, Q. coccinea, Q. prinus (now montana) 1. results * These results are generated from the intermediate files * Each line here comes from one of 1. IL_Pace_Dendro-GPP-Pheno_20260126.Rmd 2. CA_Tonzi_Dendro-GPP-Pheno_20260208.Rmd 3. VA_Pace_Dendro-GPP-Pheno_20260218.Rmd 4. NY_Lamont_Dendro-GPP-Pheno_20260222.Rmd Columns include - No - Serial Number; site - which site; year - which year - los.nd - length of season in number of days - off1.nd - offset during phase I in number of days (see table 1) - off2.nd - offset during phase II in number of days (see table 1) - Gr.Fr - Growth fraction in Phase I - Gr.P - Percent length of Phase I relative to active season (Phase I+II+III) - GPP.Fr - GPP fraction in Phase III - GPP.P - Percent length of Phase III relative to active season (Phase I+II+III) - Offset.N - Offset fraction as (Phases I+III)/Phase I + II + III - Remaining columns for micrometeorological variables at each site and eyar - Tmp - temperature in degrees Celcius - Tmp.K - temperature in degrees Kelvin - VPD - Vapour Pressure Deficit in kPa - Pr - Precipitation (mm) - SD - Standard Deviation - CV - Coefficient of Variation - QCV - Quartile CV 

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