Plant composition and weather data during tallgrass prairie restoration
Data files
Feb 20, 2026 version files 506.40 KB
Abstract
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.
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 m2 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/) 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: C3G = grass with
C3 photosynthetic pathway, C4G grass with C4 photosynthetic pathway, LF =
leguminous forb, NLF = non-leguminous forb, or UNK = unknown.
F Grow Annual (A) or perennial (P) growth.
G Photo Photosynthesis pathway (C3 or C4).
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). 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 (oC).
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 (oC).
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 (oC).
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 (oC).
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 >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 m2 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.