DOI: 10.5061/dryad.sbcc2frj5
title: Plant composition and weather data during tallgrass prairie restoration

description: Tallgrass prairie communities were restored using the same live seed mix
 every other year from 2010-2020 to test the hypothesis that dissimilarity
 in planting-year climate statistically predicts dissimilarity in
 developing plant community composition, and planting-year precipitation
 and temperature variables explain cover of species and major compositional
 underlying community dissimilarity. This dataset contains percent cover of
 plant species establishing over time in six restored prairies established
 in six different years at the Konza Prairie Biological Station in Kansas
 (USA) and corresponding precipitation and temperature data for each year a
 prairie was restored. The experiment is referred to as the “Sequential
 Restoration Plots” and contains seven prairie communities (sequences)
 restored in an agricultural field over time. Plant species, the absolute
 cover of each species, and cover of compositional groups (all sown
 species, all volunteer species, all forb species, all grass species, sown
 C4 grasses, sown C3 species, and sown forb species) over the first three
 years of community establishment in each plot and subplot of the six
 sequences corresponding to planting years 2010, 2012, 2014, 2016, 2018,
 and 2020 (sequences I-VI) are provided in this dataset. Weather data
 contained in this dataset correspond the years each prairie sequence was
 restored. Annual precipitation and temperature summary variables were
 calculated to relate cover of species and compositional groups to planting
 year climate. Climate vectors (capturing intra-annual variability and
 extremes) were also created for each planting year to perform multiple
 regression on distance matrices to examine the relationship between
 dissimilarity in planting year precipitation and temperature conditions
 and dissimilarity in communities that developed over the first three years
 of restoration.
description: Plant communities were restored every other year in a former
 agricultural field from 2010 to 2020 at the Konza Prairie Biological
 Station (KPBS) in Riley County, Kansas USA (39° 06’ 09.69” N, 96° 36’
 07.21” W). The site had been cultivated for &gt;70 years and contains
 soil classified as a silt loam formed by colluvial and alluvial deposits.
 Crop production was ceased in a 30-m x 100-m area (referred to as a
 sequence) of the agricultural field in the fall (October) prior to sowing
 prairie communities in the spring (first week of June) of a planting year
 (January-December). In each sequence, seeds of native plant species were
 sown into four 20-m x 20-m plots containing four 10-m x 10-m subplots.
 Plots were separated by a distance of 5 m. The logistical constraints of
 requiring continuous cultivation to promote the same starting soil
 conditions and the same prescribed fire regime for each plot restricted
 randomization plots assigned to different planting years throughout the
 study site. Thus, each sequence was installed adjacent to the sequence
 established the previous planting year. To foster statistical
 independence, we combined and mixed seeds separately for each plot and
 separated plots with 5-m buffer strips that were sown with a different
 composition of species. Plant species composition
 surveys were conducted in permanent circular sampling areas in each
 subplot. Species composition was recorded in late spring and late summer
 each year after planting to capture maximum cover of early and late season
 species. Only late-summer sampling was carried out in the planting year.
 To be consistent with previous studies from this experiment (Manning and
 Baer 2018, Eckhoff et al. 2023), the cover of each species was visually
 estimated in 10 m<sup>2</sup> sampling areas (1.78 m radius).
 Cover estimates were then assigned to a modified Daubenmire cover class (1
 = 0-1%, 2 = 1-5%, 3 = 5-25%, 4 = 25-50%, 5 = 50-75%, 6 = 75-95%, 7 =
 95-100%). The dataset contains the maximum cover class value of the two
 sampling times within a year, converted to the midpoint of each Daubenmire
 cover class range. Climate variables were calculated
 from weather station data collected at KPBS. Data are accessible through
 the Konza Prairie LTER Data Repository (AWE01
 dataset). From this dataset, planting-year climate summary
 variables were calculated from daily precipitation (mm) and temperature
 (°C) records. The selection of climate summary variables was based on
 potential to explain establishing plant species and communities. We chose
 precipitation and temperature in June and July (JJ) over growing season
 metrics because first-year plantings were not sown until the last week of
 May, and weather occurring in the first few months following sowing is
 critical to seed germination and establishment. Total annual
 precipitation, JJ precipitation, average yearly temperature, and JJ
 average temperature were calculated for each planting year.
 Climate vectors (capturing intra-annual variability and extremes)
 were created for each planting year to perform multivariate analyses. For
 precipitation, we created vectors of total weekly and monthly
 precipitation throughout the entire year for each planting year, as well
 as vectors of total daily and total weekly precipitation during JJ of each
 planting year. For temperature, we created vectors of average weekly and
 monthly temperature throughout the entire year for each planting year, as
 well as vectors of average daily and weekly JJ temperature during each
 planting year. 
description: # Plant composition and weather data during tallgrass prairie restoration
 [https://doi.org/10.5061/dryad.sbcc2frj5](https://doi.org/10.5061/dryad.sbcc2frj5) **DATA:**  Plant species cover and climate data are provided in one file: ## Plant_cover_and_weather_data_from_Konza_Prairie_Sequential_Restoration_Plots_years_1_2_3.xlsx). Study design: The experiment contains seven plant communities (sequences) planted in an agricultural field over time (Sequential_Restoration_Plots_Design_Figure_1.pdf). Six sequences were used for this study. Each sequence was assigned a roman numeral corresponding to the planting year (I=2010, II=2012, III=2014, IV=2016, V=2018, VI=2020, and VII=2022). Each sequence contains four 20 m x 20 m plots (numbered 1 - 4), and each plot contains four subplots (labeled A - D). Plot 4 in sequence I encroached into a shaded area, so half of the subplots (C and D) were separated from the first two (A and B). Sequence II contained fences around two subplots in each plot (randomly assigned and indicated by boxes around subplot labels). Fences were erected to exclude deer as part of a separate investigation; deer exclusion subplots were not included in this study.   **TAB: Species Cover –** Species cover data are percentages estimated in permanent 10 m^2^ circular sampling areas in the center of each subplot. The cover of each species was visually estimated in late spring and late summer each year, but only late summer in the planting year. Species were assigned to a modified Daubenmire cover class (1 = 0-1%, 2 = 1-5%, 3 = 5-25%, 4 = 25-50%, 5 = 50-75%, 6 = 75-95%, 7 = 95-100%). The maximum cover class value of the two sampling times within a year was converted to the midpoint of each Daubenmire cover class range. Species acronyms correspond to the USDA Plants Database ([http://plants.usda.gov/java/](http://plants.usda.gov/java/)) acronyms unless the species could not be identified, in which case the acronym begins with “UNK.” Column descriptions: COL.         LABEL                      DESCRIPTION A              OBS                         Observation number B              PlantingYR              Year the sequence was restored (sown) to prairie from agricultural conditions. C              Sequence                 The experiment consisted of 6 sequences (I-VI) corresponding to year planted. D              Plot                          Each sequence contained four plots (1-4). E              Subplot                    Each plot contained four subplots (A-D). F              Age                          Number of years since sowing (1-3). G             TRT                          Treatment indicates a manipulation within a subplot, relevant only to sequence II (N=no treatment and Ex = deer excluded). H             ANGE                      *Andropogon gerardii* I               BOCU                      *Bouteloua curtipendula* J               ELCA4                      *Elymus canadensis* K              PAVI2                      *Panicum virgatum* L              SCSC                        *Schizachyrium scoparium* M             SONU2                    *Sorghastrum nutans* N             AMCA6                    *Amorpha canescens* O             BAAU                      *Baptisia australis* P              DACA7                     *Dalea candida* Q             DAMU                     *Dalea multiflora* R              DAPU5                     *Dalea purpurea* S              DEIL                         *Desmanthus illnoensis* T              ECAN2                     *Echinacea angustifolia* U             HEPA19                   *Helianthus pauciflorus* V              LECA8                      *Lespedeza capitata* W            OEMA                      *Oenothera macrocarpa* X              OLRIR                      *Oligoneuron rigidum* Y              ROAR3                    *Rosa arkansana* Z              SIIN2                       *Silphium integrifolium* AA           ABTH                       *Abutilon theophrasti* AB           ACOS                       *Acalypha ostryifolia* AC            ACMI2                     *Achillea millefolium* AD           AMRE                      *Amaranthus retroflexus* AE            AMARA                   *Amaranthus* sp. AF            AMTU                      *Amaranthus tuberculatus* AG           AMAR2                    *Ambrosia artemisiifolia* AH           AMPS                      *Ambrosia psilostachya* AI             AMBRO                   *Ambrosia* sp. AJ            ANNE                      *Antennaria neglecta* AK            ARENA                    *Arenaria* sp. AL            ASSY                        *Asclepias* *syriaca* AM          AVENA                    *Avena* sp AN           BOCY                       *Boehmeria* *cylindrica* AO           BRASS2                   *Brassica* sp. AP            BREU                       *Brickellia* *eupatorioides* AQ           BRAR5                     *Bromus* *arvensis* AR           BRIN2                      *Bromus* *inermis* AS            BROMU                   *Bromus* sp. AT            BRTE                       *Bromus tectorum* AU           CABU2                     *Capsella bursa-pastoris* AV           CABR10                   *Carex brevior* AW          CEOC                       *Celtis occidentalis* AX            CELO3                      *Cenchrus longispinus* AY            CHMA15                  *Chamaesyce maculata* AZ            CHPR6                     *Chamaesyce prostrata* BA           CHSE4                     *Chamaesyce serpens* BB           CHSE6                     *Chamaesyce serpyllifolia* BC            CHAL7                     *Chenopodium album* BD           CHENO                    *Chenopodium* sp. BE            CHVE2                     *Chloris verticillata* BF            CIAL2                       *Cirsium altissimum* BG           CIDI                          *Cirsium discolor* BH           CIRSI                        *Cirsium* sp. BI             CIUN                        *Cirsium undulatum* BJ            COMA2                    *Conium maculatum* BK            COCA5                     *Conyza canadensis* BL            CROTO                    *Croton* sp. BM          DALEA                     *Dalea* sp. BN           DACA6                     *Daucus* *carota* BO           DECA7                     *Desmodium canadense* BP            DISA                        *Digitaria sanguinalis* BQ           ECCR                        *Echinochloa* *crus-galli* BR           ECHIN4                    *Echinochloa* sp. BS            ELEUS                      *Eleusine* sp. BT            ELNY                        *Ellisia nyctelea* BU           ELYMU                    *Elymus* sp. BV           ERCI                         *Eragrostis cilianensis* BW          ERAGR                    *Eragrostis* sp. BX            ERSP                        *Eragrostis* *spectabillis* BY            ERAN                       *Erigeron* *annuus* BZ            ERIGE2                    *Erigeron* sp. CA            EUAL3                     *Eupatorium altissimum* CB            EUMA8                    *Euphorbia marginata* CC            FESTU                     *Festuca* sp. CD            GAAP2                     *Galium* *aparine* CE            GALIU                      *Galium* sp. CF            GLEDI                      *Gleditsia* sp. CG           GLTR                       *Gleditsia triacanthos* CH           HEHI                        *Hedeoma hispida* CI             HEAN3                    *Helianthus annuus* CJ             HELIA3                    *Helianthus* sp. CK            HIBIS2                     *Hibiscus* sp. CL            HITR                        *Hibiscus trionum* CM           HOPU                      *Hordeum pusillum* CN           HORDE                    *Hordeum* sp. CO           HOVU                      *Hordeum* *vulgare* CP            KUST                       *Kummerowia stipulacea* CQ           LASE                        *Lactuca serriola* CR            LACTU                     *Lactuca* sp. CS            LATA                        *Lactuca tatarica* CT            LAAM                      *Lamium amplexicaule* CU           LAOC3                     *Lappula occidentalis* CV            LEPID                       *Lepidium* sp. CW          LEVI6                       *Lespedeza violacea* CX            MESA                      *Medicago sativa* CY            MEDIC                     *Medicago* sp. CZ            MINY                       *Mirabilis nyctaginea* DA           MOVE                      *Mollugo verticillata* DB           MORUS                   *Morus* sp. DC            OECU2                     *Oenothera curtiflora* DD           OXALI                      *Oxalis* sp. DE            OXST                       *Oxalis stricta* DF            PACA6                     *Panicum capillare* DG           PADI                        *Panicum dichotomiflorum* DH           PANIC                      *Panicum* sp. DI             PHVI5                      *Physalis virginiana* DJ            PLMA2                     *Plantago major* DK            PLPA2                      *Plantago patagonica* DL            PLANT                     *Plantago* sp. DM          POPR                       *Poa* *pratensis* DN           POA                         *Poa* sp. DO           PODE3                     *Populus deltoides* DP            POOL                       *Portulaca oleracea* DQ           POTEN                     *Potentilla* sp. DR           PSOB3                     *Pseudognaphalium obtusifolium* DS            PSTE5                      *Psoralidium tenuiflorum* DT            RACO3                     *Ratibida columnifera* DU           RHGL                       *Rhus glabra* DV           RHUS                      *Rhus* sp. DW          RUBUS                    *Rubus* sp. DX            RUHI2                     *Rudbeckia hirta* DY            SAAZ                       *Salvia azurea* DZ            SALVI                       *Salvia* sp. EA            SEPU8                     *Setaria pumila* EB            SETAR                     *Setaria* sp. EC            SEVI4                       *Setaria* *viridis* ED            SOCA3                     *Solanum carolinense* EE            SOPT7                     *Solanum ptycanthum* EF            SORO                      *Solanum rostratum* EG           SOLAN                     *Solanum* sp. EH           SOAL6                     *Solidago altissima* EI             SOLID                      *Soligado* sp. EJ             SOHA                      *Sorghum halepense* EK            SYOR                       *Symphoricarpos orbiculatus* EL            SYER                        *Symphyotrichum ericoides* EM           SYMPH4                  *Symphyotrichum* sp EN           TAOF                       *Taraxacum* *officinale* EO           TORA2                     *Toxicodendron radicans* EP            TRDU                       *Tragopogon dubius* EQ           TRTE                        *Tribulus terrestris* ER            TRRE3                     *Trifolium repens* ES            TRPE4                      *Triodanis perfoliata* ET            TRIOD                      *Triodanis* sp. EU           ULAM                      *Ulmus americana* EV            ULMUS                    *Ulmus* sp. EW          VETH                       *Verbascum thapsus* EX            VERBE                     *Verbena* sp. EY            VEST                        *Verbena stricta* EZ            VEUR                       *Verbena urticifolia* FA            VEBA                       *Vernonia baldwinii* FB            VERNO                    *Vernonia* sp. FC            VEAR                       *Veronica arvensis* FD            VERON                    *Veronica* sp. FE            XAST                        *Xanthium strumarium* FF            ZEMA                      *Zea mays* FG           UNK1                       Unknown FH           UNK2                       Unknown FI             UNK3                       Unknown FJ             UNK4                       Unknown FK            UNK5                       Unknown FL            UNK6                       Unknown FM           UNK8                       Unknown FN           UNK9                       Unknown FO           UNK10                     Unknown FP            UNK11                     Unknown FQ           UNK12                     Unknown FR            UNK13                     Unknown FS            UNK14                     Unknown FT            UNK15                     Unknown                 **TAB: Species Metadata –** This tab contains species codes (USDA Plants database); scientific names; classifications according to whether they were sown (or volunteer), grass (or forb), broad functional group, growth form, and photosynthesis pathway; and notes about unknowns. Column descriptions:   COL.         LABEL                      DESCRIPTION A              Code                        Species identifier corresponding to USDA Plants Database abbreviation. B              Name                      Species name. C              Source                     Sown from the seed mix versus volunteer. D              Type                        Grass or forb. E              Group                      Species assigned to broad functional groups: C~3~G = grass with                                                                          C~3~ photosynthetic pathway, C~4~G grass with C~4~ photosynthetic pathway, LF =                                                 leguminous forb, NLF = non-leguminous forb, or UNK = unknown. F              Grow                       Annual (A) or perennial (P) growth. G             Photo                       Photosynthesis pathway (C~3~ or C~4~). H             Notes                      Notes about unknown species.   ** ** **TAB: Raw Climate Data –** Planting-year summary climate variables and climate vectors were calculated for each sequence (I-VI) from total daily precipitation (mm) and average daily temperature (°C) collected at Konza Prairie Biological Station in Manhattan, Kansas USA ([https://lter.konza.ksu.edu/content/awe012](https://lter.konza.ksu.edu/content/awe012)). Summary climate variables used in this study included total precipitation in the planting year, total precipitation in June and July each planting year, annual average planting year temperature, and average temperature in June and July of each planting year (corresponding to Table 1). Climate vectors (capturing intra-annual variability and extremes) were created for each planting year to perform multivariate analyses (corresponding to Table 2). We created vectors of total weekly and monthly precipitation throughout the entire year for each planting year, as well as vectors of total daily and total weekly precipitation during June and July of each planting year. For temperature, we created vectors of average weekly and monthly temperature throughout the entire year for each planting year, as well as vectors of average daily and weekly JJ temperature during each planting year. Column descriptions:   COL.         LABEL                      DESCRIPTION A              KNZDATA Konza      Prairie climate dataset name. B              PlantingYR              Year the sequence was restored to prairie from agricultural conditions. C              Sequence                 The experiment consisted of 6 sequences (I-VI) corresponding to year planted. D              MonthDAY               Day of month. E              YearDAY                  Day of year. F              TAVE                       Average daily temperature (°C). G             DPPT                        Total daily precipitation (mm).   ** ** **TAB: Summary Climate Variables –** Planting-year summary precipitation (PPT) and temperature (T) variables on an annual basis (YR) or in June and July (JJ)  from for each planting year that used in simple linear regressions. Column descriptions:   COL.         LABEL                      DESCRIPTION A              Year                         Year climate data are summarized corresponding to planting year. B              YR_PPT                    Total annual precipitation (mm) C              JJ_PPT                     Total precipitation received in June and July (mm) D              YR_AvgTemp          Annual average daily temperature (°C) E              JJ_AvgTemp            Average daily temperature in June and July (°C) Day of year F              Sequence                The experiment consisted of 6 sequences (I-VI) corresponding to year planted. G             Plot                          Each sequence contained four plots (1-4). ** ** **TAB: Climate Vector Wk YR PPT –** Climate vector of weekly (Wk) precipitation each planting year used in matrix regression and multiple matrix regression modeling. Column descriptions   COL.         LABEL                      DESCRIPTION A              PlantingYR              Year the sequence was restored to prairie from agricultural conditions B              Plot                          Each sequence contained four plots (1-4) C-BC        Week 1-53              Total precipitation received in weeks 1-53 of the planting year (mm).     **TAB: Climate Vector Wk YR AvgT :** Climate vector of weekly (Wk) average temperature each planting year used in matrix regression and multiple matrix regression modeling. Column descriptions:   COL.         LABEL                      DESCRIPTION A              PlantingYR              Year the sequence was restored to prairie from agricultural conditions B              Plot                          Each sequence contained four plots (1-4) C-BC        Week 1-53              Average temperature in week 1-53 of the corresponding planting year (^o^C).   ** ** **TAB: Climate Vector Wk JJ PPT –** Climate vector of weekly (Wk) precipitation received in June and July each planting year used in matrix regression and multiple matrix regression modeling. Column descriptions:   COL.         LABEL                      DESCRIPTION A              PlantingYR              Year the sequence was restored to prairie from agricultural conditions B              Plot                          Each sequence contained four plots (1-4) C...L         Week 22-31            Total precipitation received in week 22-31 corresponding with June and July in                                                 the planting year (mm). ** ** ** ** **TAB: Climate Vector Wk JJ AvgT –** Climate vector of weekly (Wk) average temperature in June and July each planting year used in matrix regression and multiple matrix regression modeling. Column descriptions:   COL.         LABEL                      DESCRIPTION A              PlantingYR              Year the sequence was restored to prairie from agricultural conditions B              Plot                          Each sequence contained four plots (1-4) C...L         Week 22-31            Weekly average temperature in weeks 22-31 corresponding with June and July                                                 in the planting year (^o^C).     **TAB: Climate Vector Mo YR PPT –** Climate vector of monthly (Mo) precipitation received April-September each planting year used in matrix regression and multiple matrix regression modeling. Column descriptions:   COL.         LABEL                      DESCRIPTION A              PlantingYR              Year the sequence was restored to prairie from agricultural conditions B              Plot                          Each sequence contained four plots (1-4) C...L         Week 22-31            Total precipitation received in week 22-31 corresponding with June and July in                                                 the planting year (mm)   **TAB: Climate Vector Mo YR AvgT –** Climate vector of monthly (Mo) average temperature received April-September each planting year used in matrix regression and multiple matrix regression modeling. Column descriptions:   COL.         LABEL                      DESCRIPTION A              PlantingYR              Year the sequence was restored to prairie from agricultural conditions B              Plot                          Each sequence contained four plots (1-4) C...L         Week 22-31            Total precipitation received in week 22-31 corresponding with June and July in                                                 the planting year (^o^C).   **TAB: Climate Vector D JJ PPT –** Climate vector of daily (D) precipitation received in June and July each planting year used in matrix regression and multiple matrix regression modeling. Column descriptions:   COL.         LABEL                      DESCRIPTION A              PlantingYR              Year the sequence was restored to prairie from agricultural conditions B              Plot                          Each sequence contained four plots (1-4) C-BJ         Day 153-212            Total precipitation received days 153-212 corresponding with June and July in                                                 the planting year (mm). ** ** ** ** **TAB: Climate Vector D JJ AvgT –** Climate vector of weekly (Wk) average temperature in June and July each planting year used in matrix regression and multiple matrix regression modeling. Column descriptions:   COL.         LABEL                      DESCRIPTION A              PlantingYR              Year the sequence was restored to prairie from agricultural conditions B              Plot                          Each sequence contained four plots (1-4) C-BJ         Day 153-212            Average daily temperature for days 153-212 corresponding with June and July                                                 in the planting year (^o^C).