Woody plant diversity before and after the Horseshoe Two Fire in the Chiricahua Mountains, Arizona, USA
Data files
Apr 25, 2024 version files 52.45 KB
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DiversityDryad.csv
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README.md
Abstract
Aim: Drastic changes in fire regimes are altering plant communities, inspiring ecologists to better understand the relationship between fire and plant species diversity. We examined the impact of a 90,000-ha wildfire on woody plant species diversity in an arid mountain range in southern Arizona, USA. We tested recent fire-diversity hypotheses by addressing the impacts of fire severity, fire variability, historical fire regimes, and topography on diversity.
Location: Chiricahua National Monument, Chiricahua Mountains, Arizona. USA., part of the Sky Islands of the US-Mexico borderlands.
Taxon: Woody plant species.
Methods: We sampled woody plant diversity in 138 plots before (2002-2003) and after (2017-2018) the 2011 Horseshoe Two Fire in three vegetation types and across fire severity and topographic gradients. We calculated gamma, alpha, and beta diversity and examined changes over time in burned vs. unburned plots and the shapes of the relationships of diversity with fire severity and topography.
Results: Alpha species richness declined and beta and gamma diversity increased in burned but not unburned plots. Fire-induced enhancement of gamma diversity was confined to low fire severity plots. Alpha diversity did not exhibit a clear continuous relationship with fire severity. Beta diversity was enhanced by fire severity variation among plots and increased with fire severity up to very high severity, where it declined slightly.
Main Conclusions: The results reject the intermediate disturbance hypothesis for alpha diversity but weakly support it for gamma diversity. Spatial variation in fire severity promoted variation among plant assemblages, supporting the pyrodiversity hypothesis. Long-term drought probably amplified fire-driven diversity changes. Despite the apparent benign impact of the fire on diversity, the replacement of two large conifer species with a suite of drought-tolerant shrubs signals the potential loss of functional diversity, a pattern that may warrant restoration efforts to retain these important compositional elements.
README: Woody plant diversity before and after the Horseshoe Two Fire in the Chiricahua Mountains, Arizona, USA
https://doi.org/10.5061/dryad.5x69p8d3w
Data set with plots, pre-fire vs. post-fire rows, environmental predictor variables, and abundance of all woody plant species.
Description of the data and file structure
It's easy to navigate this data set. The main structure includes rows for before the mega fire (pre-fire) and after the fire (post-fire) for each plot (numbered in the first column). For each plot x post- or pre-fire, there are columns of data on environmental predictors: vegetation type, location, categorical fire severity (including no fire), continuous fire severity (dNBR), elevation (m), topographic position, and TRMI (topographic relative moisture index).
There are three vegetation types -- juniper, pinyon, and pine-oak -- that were determined by a cluster analysis of the abundance of species across all plots.
Fire severity categories (fire severity) and continuous fire severity (dNBR) were derived from the differenced normalized burn ratio (dNBR; Eidenshink et al., 2007), a Landsat ETM+-derived product that estimates change in fire severity from the month prior to 6 months after a fire. The ratio is calculated from ETM + bands 4 and 7 as (ETM4–ETM7)/(ETM4 + ETM7), where ETM4 is the near-infrared spectral range (0.76–0.90 μm), and ETM7, the shortwave infrared spectral range (2.08–2.35 μm). Differenced NBR images (post-fire NBR subtracted from pre-fire NBR) are referred to as dNBR images. We acquired dNBR data from the Monitoring Trends in Burn Severity data distribution site (https://www.mtbs.gov/) for each plot in QGIS. One column in the dataset is continuous dNBR; "fireseverity" is dNBR grouped into fire severity classes (none, low, moderate, and high).
Topographic relative moisture index (TRMI) provides an index of relative soil moisture available, especially useful when it is difficult to directly measure this metric. TRMI was calculated from field-measured topographic position (ridge, upper elevation, middle elevation, lower elevation, and valley), slope direction (in degrees), slope steepness (in degrees), and surface shape (convex, convex-straight, straight, concave-straight, and concave).
Elevation was estimated from 30-m digital elevation models in QGIS. Topographic position (ridge, upper elevation, middle elevation, lower elevation, and valley) was determined in the field.
The reminder of the data columns are the number of stems per ha for all woody plant species. The species are indicated by four-letter codes (five for two species with the same four-letter code). The species identification for each code is given below, using the plants.usda.gov database.
plants.usda.gov | |
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CODE | NEW SPECIES NAMES |
AGPAL | Agave palmeri Engelm. |
AGPAR | Agave parryi Engelm. |
AMFR | Amorpha fruticosa L. |
ARAR | Arbutus arizonica (Gray) Sarg. |
ARPU | Arctostaphylos pungens H.B.K. |
BASE | Baccharis sergiloides A. Gray |
BOTE | Bouvardia ternifolia (Cav.) Schltdl. |
BRCA | Brickellia californica (T. and G.) Gray |
CEFE | Ceanothus fendleri Gray |
CEMO | Cercocarpus montanus Raf. var. montanus |
CHSP | Chloracantha spinosa (Benth.) G.L. Nesom |
CLPS | Clematis pseudoalpina (Nutt.) Torr. & A. Gray var. columbiana |
CYSP | Cylindropuntia spinosior (Engelm.) F.M. Knuth |
DAFO | Dalea formosa Torr. |
DAWR | Dasylirion wheeleri Wats. |
ECCO | Echinocereus coccineus |
ECRI | Echinocereus rigidissimus var. rigidissimus |
ECTR | Echinocereus triglochidiatus Engelm. |
ERLA | Ericameria laricifolia (Gray) Shinners (Haplopappus laricifolius Gray) |
FAPA | Fallugia paradoxa (D. Don) Endl. ex Torr. |
FRPA | Fraxinus pennsylvanica ssp. velutina (Torr.) G.N. Miller |
GAWR | Garrya wrightii Torr. |
GUSA | Gutierrezia sarothrae (Pursh) Britton & Rusby |
HEAR | Hesperocyparis arizonica (Greene) Bartel |
JUDE | Juniperus deppeana Steud. |
JUMA | Juglans major (Torr.) A. Heller |
MIBI | Mimosa biuncifera Ortega var. biuncifera (Benth.) Barneby |
NOMI | Nolina microcarpa Wats. |
OPCH | Opuntia chlorotica Engelm. and Bigel. |
PHMI | Philadelphus microphyllus A. Gray |
PIAR | Pinus arizonica Engelm. (P. ponderosa var. arizonica Shaw) |
PIDI | Pinus discolor Bailey and Hawksw. (P. cembroides var. bicolor Little) |
PIED | Pinus edulis Engelm. |
PIEN | Pinus engelmannii Carr. (P. latifolia Sarg.) |
PILE | Pinus leiophylla var. chihuahuana (Engelm.) Shaw |
PLWR | Platanus wrightii S. Watson |
PRSE | Prunus serotina Ehrh. var. virens (Wooton & Standl.) McVaugh |
PRVI | Prunus virginiana L. var. demissa (Nutt.) Torr. |
PSME | Pseudotsuga menziesii var. glauca (Beissn.) Franco |
PTTR | Ptelea trifoliata L. ssp. angustifolia (Benth.) V. Bailey var. angustifolia (Benth.) M.E. Jones |
QUAR | Quercus arizonica Sarg. |
QUEM | Quercus emoryi Torr. |
QUGA | Quercus gambelii Nutt. |
QUHY | Quercus hypoleucoides Camus |
QUPU | Quercus pungens Liebm. |
QURU | Quercus rugosa Nee. (Q. reticulata) |
QUTO | Quercus toumeyi Sarg. |
RHBE | Rhamnus betulaefolia Greene |
RHMI | Rhus microphylla Engelm. ex A. Gray |
RHTR | Rhus trilobata Nutt. |
RHVI | Rhus virens Lindh. ex A. Gray |
RONE | Robinia neomexicana Gray |
TORY | Toxicodendron rydbergii (Small ex Rydb.) Greene |
VIAR | Vitis arizonica Engelm. |
YUSC | Yucca schottii Engelm. |
Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z., Quayle, B., & Howard, S. (2007). A project for monitoring trends in burn severity. Fire Ecology, 3(4), 3–21. https://doi.org/10.4996/fireecology.0301003
Parker, A. J. (1982). The topographic relative moisture index: an approach to soil-moisture assessment in mountain terrain. Physical Geography, 3(2), 160–168. https://doi.org/10.1080/02723 646.1982.10642224
Sharing/Access information
Authors are happy to help anyone with more details on these data.
Methods
Woody plant presence and abundance sampled in 138 plots before and after the fire.
Usage notes
No missing data. Nearly all columns are self-explanatory. Additional information can be found in the publication or through contacting authors.