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Cover–biomass relationships of an invasive annual grass, Bromus rubens, in the Mojave Desert

Published online by Cambridge University Press:  04 November 2020

Scott R. Abella Open the ORCID record for Scott R. Abella [Opens in a new window]
Scott R. Abella*
Affiliation:
Associate Professor, School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
*
Author for correspondence: Scott R. Abella, School of Life Sciences, University of Nevada Las Vegas, 4505 South Maryland Parkway, Las Vegas, NV 89154-4004. (Email: [email protected])
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Abstract

Estimates of plant biomass are helpful for many applications in invasive plant science and management, but measuring biomass can be time-consuming, costly, or impractical if destructive sampling is inappropriate. The objective of this study was to assess feasibility of developing regression equations using a fast, nondestructive measure (cover) to estimate aboveground biomass for red brome (Bromus rubens L.), a widespread nonnative annual grass in the Mojave Desert, USA. At three study sites, including one measured for three consecutive years, B. rubens cover spanned 0.1% to 85% and aboveground biomass 1 to 321 g m−2. In log10-transformed linear regressions, B. rubens cover accounted for 68% to 96% of the variance in B. rubens biomass among sites, with all coefficients of determination significant at P < 0.05. For every doubling of percent cover, biomass was predicted to increase by 78%, 83%, and 144% among the three sites. At the site measured for three consecutive years, which ranged in rainfall from 65% to 159% of the long-term average, regression slopes each year differed from other years. Regression results among sites were insensitive to using cover classes (10 classes encompassing 0% to 100% cover) compared with simulated random distribution of integer cover within classes. Biomass of B. rubens was amenable to estimation in the field using cover, and such estimates may have applications for modeling invasive annual plant fuel loads and ecosystem carbon storage.

Keywords

Allometric equationsdouble samplingfuel modelingred bromewildfire
Type
Note
Information
Invasive Plant Science and Management , Volume 13 , Issue 4 , December 2020 , pp. 288 - 292
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of the Weed Science Society of America

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Footnotes

Associate Editor: Steven S. Seefeldt, Washington State University

References

Abella, SR, Embrey, TM, Schmid, SM, Prengaman, KA (2012) Biophysical correlates with the distribution of the invasive annual red brome (Bromus rubens) on a Mojave Desert landscape. Invasive Plant Sci Manag 5:47–56 CrossRefGoogle Scholar
Alaback, PB (1986) Biomass regression equations for understory plants in coastal Alaska: effects of species and sampling design on estimates. Northwest Sci 60:90–103 Google Scholar
Andrade, JM, Estévez-Pérez, MG (2014) Statistical comparison of the slopes of two regression lines: a tutorial. Analytica Chimica Acta 838:1–12 CrossRefGoogle ScholarPubMed
Axmanová, I, Tický, L, Fajmonova, Z, Hájková, P, Hettenbergerová, E, Li, C, Merunková, K, Nejezchlebová, M, Otýpková, Z, Vymazalová, M, Zelený, D (2012) Estimation of herbaceous biomass from species composition and cover. Appl Veg Sci 15:580–589 CrossRefGoogle Scholar
Beatley, JC (1966) Ecological status of introduced brome grasses (Bromus spp.) in desert vegetation of southern Nevada. Ecology 47:548–554 CrossRefGoogle Scholar
Brooks, ML (2000) Bromus madritensis ssp. rubens (L.) Husnot. Pages 72–76 in Bossard, CC, Randall, JM, Hoshovsky, MC, eds. Invasive Plants of California’s Wildlands. Berkeley: University of California Press. 360 p Google Scholar
Brooks, ML, Minnich, RA, Matchett, JR (2018) Southeastern deserts bioregion. Pages 353–378 in van Wagtendonk, JW, Sugihara, NG, Stephens, SL, Thode, AE, Shaffer, KE, Fites-Kaufman, JA, eds. Fire in California’s Ecosystems. Berkeley: University of California Press. 568 p Google Scholar
Casady, GM, van Leeuwen, WJD, Reed, BC (2013) Estimating winter annual biomass in the Sonoran and Mojave Deserts with satellite- and ground-based observations. Remote Sens 5:909–926 CrossRefGoogle Scholar
Catchpole, WR, Wheeler, CJ (1992) Estimating plant biomass: a review of techniques. Austr J Ecol 17:121–131 CrossRefGoogle Scholar
Chieppa, J, Power, SA, Tissue, DT, Nielsen, UN (2020) Allometric estimates of aboveground biomass using cover and height are improved by increasing specificity of plant functional groups in eastern Australian rangelands. Range Ecol Manag 73:375–383 CrossRefGoogle Scholar
Elzinga, CL, Salzer, DW, Willoughby, JW (1998) Measuring and Monitoring Plant Populations. BLM Technical Reference 1730-1. Denver, CO: Bureau of Land Management. 477 pGoogle Scholar
Hammer, Ø (2020) PAST 4.02, Paleontological Statistics Reference Manual. Oslo, Norway: University of Oslo. 280 p Google Scholar
Hermy, M (1988) Accuracy of visual cover assessments in predicting standing crop and environmental correlation in deciduous forests. Vegetatio 75:57–64 CrossRefGoogle Scholar
Humphrey, LD (1985) Use of biomass predicted by regression from cover estimates to compare vegetational similarity of sagebrush-grass sites. Great Basin Nat 45:94–98 Google Scholar
Huxman, TE, Hamerlynck, EP, Smith, SD (1999) Reproductive allocation and seed production in Bromus madritensis spp. rubens at elevated atmospheric CO2. Funct Ecol 13:769–777 CrossRefGoogle Scholar
Jurand, BS, Abella, SR (2013) Soil seed banks of the exotic annual grass Bromus rubens on a burned desert landscape. Rangeland Ecol Manag 66:157–163 CrossRefGoogle Scholar
MacDonald, RL, Burke, JM, Chen, HYH, Prepas, EE (2012) Relationship between aboveground biomass and percent cover of ground vegetation in Canadian boreal plain riparian forests. For Sci 58:47–53 Google Scholar
Muukkonen, P, Mäkipää, R, Laiho, R, Minkkinen, K, Vasander, H, Finér, L (2006) Relationship between biomass and percentage cover in understorey vegetation of boreal coniferous forests. Silva Fennica 40:231–245 CrossRefGoogle Scholar
Ónodi, G, Kertész, M, Kovács-Láng, E, Ódor, P, Botta-Dukát, Z, Lhotsky, B, Barabás, S, Mojzes, A, Kröel-Dulay, G (2017) Estimating aboveground herbaceous plant biomass via proxies: the confounding effects of sampling year and precipitation. Ecol Indicators 79:355–360 CrossRefGoogle Scholar
Peet, RK, Wentworth, TR, White, PS (1998) A flexible, multipurpose method for recording vegetation composition and structure. Castanea 63:262–274 Google Scholar
Rao, LE, Allen, EB (2010) Combined effects of precipitation and nitrogen deposition on native and invasive winter annual production in California deserts. Oecologia 162:1035–1046 CrossRefGoogle ScholarPubMed
Rudgers, JA, Hallmark, A, Baker, SR, Baur, L, Hall, KM, Litvak, ME, Muldavin, EH, Pockman, WT, Whitney, KD (2019) Sensitivity of dryland plant allometry to climate. Funct Ecol 33:2290–2303 CrossRefGoogle Scholar
Salo, LF (2005) Red brome (Bromus rubens subsp. madritensis) in North America: possible modes for early introductions, subsequent spread. Biol Invasions 7:165–180 CrossRefGoogle Scholar
Smith, SD, Charlet, TN, Zitzer, SF, Abella, SR, Vanier, CH, Huxman, TE (2014) Long-term response of a Mojave Desert winter annual plant community to a whole-ecosystem atmospheric CO2 manipulation (FACE). Global Change Biol 20:879–892 Google Scholar
Snowdon, P (1991) A ratio estimator for bias correction in logarithmic regressions. Can J For Res 21:72–724.CrossRefGoogle Scholar
Tausch, RJ (1989) Comparison of regression methods for biomass estimation of sagebrush and bunchgrass. Great Basin Nat 49:373–380 Google Scholar
Van Linn, PF, Nussear, KE, Esque, TC, DeFalco, LA, Inman, RD, Abella, SR (2013) Estimating wildfire risk on a Mojave Desert landscape using remote sensing and field sampling. Int J Wildland Fire 22:770–779 CrossRefGoogle Scholar
Wu, KK, Jain, SK (1978) Genetic and plastic responses in geographic differentiation of Bromus rubens populations. Can J Bot 56:873–879 CrossRefGoogle Scholar
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