Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-26T10:17:51.238Z Has data issue: false hasContentIssue false
  • English
  • Français

Allometric equations for the invasive vine air potato (Dioscorea bulbifera) in its exotic range in Florida

Published online by Cambridge University Press:  14 May 2020

Min B. Rayamajhi Open the ORCID record for Min B. Rayamajhi [Opens in a new window] ,
Eric Rohrig ,
Philip W. Tipping ,
Paul D. Pratt  and
Jorge G. Leidi
Min B. Rayamajhi*
Affiliation:
Research Plant Pathologist, USDA–ARS, Invasive Plant Research Laboratory, Fort Lauderdale, FL, USA
Eric Rohrig
Affiliation:
Methods Development and Biological Control, Florida Department of Agriculture, Gainesville, FL, USA
Philip W. Tipping
Affiliation:
Research Entomologist, USDA–ARS, Invasive Plant Research Laboratory, Fort Lauderdale, FL, USA
Paul D. Pratt
Affiliation:
Research Entomologist, USDA–ARS–WRRC, Albany, CA, USA
Jorge G. Leidi
Affiliation:
Biological Research Technician, USDA–ARS, Invasive Plant Research Laboratory, Fort Lauderdale, FL, USA
*
Author for correspondence: Min B. Rayamajhi, USDA/ARS, Invasive Plant Research Laboratory, Fort Lauderdale, FL33314. (Email: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Nondestructive means for estimating air potato (also known as air yam; Dioscorea bulbifera L.) biomass will help gauge its management efficacy over time. We developed allometric equations to estimate total and fractional biomass components and densities of aerial bulbils and underground tubers of field-grown D. bulbifera in Florida. We selected four naturally infested sites representing its southern, central, and northern distribution in Florida and measured three independent variables (vine densities, stem diameters, and top heights) of 84 (21 site−1) discrete D. bulbifera patches during late October to early December of 2012. We destructively harvested D. bulbifera biomass, sorted by tubers, stems, leaves, and bulbils; counted units of bulbils and underground tubers (dependent variables); and dried to a constant weight. Mean percentages of tuber, stem, leaf, and bulbil fractions in total biomass were 42.0, 15.6, 26.0, and 16.4, respectively. We developed a parameterized multiplicative prediction model and regression equation for each dependent variable. Slopes of relationships among independent and dependent variables varied by biomass and density (bulbil and tuber) of plant components. Multiplied values of independent variables: all three for total, tuber, stem, and leaf biomass; two (vine base diameter*patch height) for bulbil biomass; two (vine density*patch height) for bulbil density; and only one (stem density) for tuber density provided best (R2-based) prediction values. These models will provide nondestructive methods for estimating biomass components and density of vegetative propagules of naturally growing D. bulbifera. Models are critical for understanding the performance of D. bulbifera in its exotic range, estimating biomass to project control costs, and comparing biomass components and bulbil/tuber densities during pre- and postmanagement periods to gauge control efficacy.

Keywords

Above- and belowground componentsbiomass prediction equationsbulbilsexotic vinespredictive modelstubers
Type
Research Article
Information
Invasive Plant Science and Management , Volume 13 , Issue 2 , June 2020 , pp. 76 - 83
Creative Commons
Creative Common License - CCCreative Common License - BY
This is a work of the U.S. Government and is not subject to copyright protection in the United States.
Copyright
© Weed Science Society of America, 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Associate Editor: Darren J. Kriticos, CSIRO Ecosystem Sciences

References

Addo-Fordjour, P, Rahmad, ZB, (2013) Development of allometric equations for estimating aboveground liana biomass in tropical primary and secondary forests, Malaysia. Int J Ecol 2013:1–8Google Scholar
Baskerville, GL (1972) Use of logarithmic regression in the estimation of plant biomass. Can J For Res 2:49–53CrossRefGoogle Scholar
Beedlow, PA, Tingey, DT, Phillips, DL, Hogsett, WE, Olszyk, DM (2004) Rising atmospheric CO2 and carbon sequestration in forests. Front Ecol Environ 2:315–322Google Scholar
Bindi, M, Fibbi, L, Miglietta, F (2001) Free air CO2 enrichment (FACE) of grapevine (Vitis vinifera L.): II. Growth and quality of grape and wine in response to elevated CO2 concentrations. Eur J Agron 14:145–155CrossRefGoogle Scholar
Burkill, IH (1960) The organography and the evolution of Dioscorea, the family of yams. Bot J Linn Soc 56:319–412CrossRefGoogle Scholar
Case, BS, Hall, RJ (2008) Assessing prediction errors of generalized tree biomass and volume equations for the boreal forest region of west-central Canada. Can J For Res 38:878–889CrossRefGoogle Scholar
Castelan-Estrada, MP, Vivin, P, Gaudiliére, JP (2002) Allometric relationships to estimate seasonal aboveground vegetative and reproductive biomass of Vitis vinifera L. Ann Bot 89:401–408CrossRefGoogle Scholar
Center, TD, Rayamajhi, M, Dray, FA, Madeira, PM, Witkus, G, Rohrig, E, Mattison, E, Lake, E, Smith, M, Zhang, J, Purcell, M, Konstantinov, A, Schmitz, D (2013) Host range validation, molecular identification and release and establishment of a Chinese biotype of the Asian leaf beetle Lilioceris cheni (Coleoptera: Chrysomelidae: Criocerinae) for control of Dioscorea bulbifera L. in the southern United States. Biocontrol Sci Technol 23:735–755CrossRefGoogle Scholar
Coursey, DG (1967) Yams: An Account of the Nature, Origins, Cultivation and Utilization of the Useful Members of the Dioscoreaceae. London: Longmans. 230 pGoogle Scholar
Croxton, M, Andreu, M, Williams, D, Overholt, W, Smith, J (2011) Source and diversity of air-potato (Dioscorea bulbifera) in Florida. Invasive Plant Sci Manag 4:22–30CrossRefGoogle Scholar
Elzein, TM, Blarquez, O, Gauthier, O, Carcaillet, C (2011) Allometric equations for biomass assessment of subalpine dwarf shrubs. Alpine Bot 121:129–134CrossRefGoogle Scholar
Erickson, KD, Pratt, PD, Rayamajhi, MB, Horvitz, CC (2017) Introduction history influences aboveground biomass allocation in Brazilian peppertree (Schinus terebinthifolius). Invasive Plant Sci Manag 10:247–253CrossRefGoogle Scholar
Freund, RJ, Little, RC (2000) SAS System for Regression. 3rd ed. Cary, NC: SAS Institute. 236 pGoogle Scholar
Gehring, C, Park, S, Denich, M (2004). Liana allometric biomass equations for Amazonian primary and secondary forest. For Ecol Manag 195:69–83CrossRefGoogle Scholar
Gutierrez, AP, Williams, DW, Kido, H (1985) A model of grape growth and development: the mathematical structure and biological considerations. Crop Sci 25:721–728CrossRefGoogle Scholar
Horvitz, CC, Koop, A (2001). Removal of non-native vines and post-hurricane recruitment in tropical hardwood forests of Florida. Biotropica 33:268–281CrossRefGoogle Scholar
Huff, S, Ritchie, M, Temesgen, H (2017) Allometric equations for estimating aboveground biomass for common shrubs in northeastern California. For Ecol Manag 398:48–63CrossRefGoogle Scholar
Laroche, FB, McKim, J (2004) Cost comparison of melaleuca treatment methods. Wildland Weeds 7:12–15Google Scholar
Lieurance, DM (2007) Biomass allocation of the invasive tree Acacia auriculiformis and refoliation following hurricane-force winds. J Torrey Bot Soc 134:389–397CrossRefGoogle Scholar
Litton, CM, Kauffman, JB (2008) Allometric models for predicting aboveground biomass in two widespread woody plants in Hawaii. Biotropica 40:313–320CrossRefGoogle Scholar
Liu, CJ, Westman, CJ (2009) Biomass in a Norway spruce–Scots pine forest: a comparison of estimation methods. Boreal Environ Res 14:875–888Google Scholar
Martin, FW (1974) Tropical Yams and Their Potential: Part 2. Dioscorea bulbifera. Agriculture Handbook No. 466. Washington, DC: U.S. Department of Agriculture, Agriculture Research Service in cooperation with U.S. Agency for International Development. 20 pGoogle Scholar
McGinnis, TW, Shook, CD, Keeley, JE (2010) Estimating aboveground biomass for broadleaf woody plants and young conifers in Sierra Nevada, California, Forests. West J Appl For 25:203–209CrossRefGoogle Scholar
Overholt, WA, Markle, L, Meisenberg, M, Raz, L, Wheeler, G, Pemberton, R, Taylor, J, King, M, Schmitz, D, Parks, GR, Rayamajhi, M, Rohrig, E, Lake, E, Smith, M, Hibbard, K, et al. (2014) Air Potato Management Plan for Florida. Florida Exotic Pest Plant Council. http://plants.ifas.ufl.edu/wp-content/uploads/files/caip/pdfs/AirPotatoManagementPlanv8_updatedFeb2014Version_corrected_Oct_2014.pdf.. Accessed: March 22, 2019Google Scholar
Putz, FE (1983) Liana biomass and leaf area of a “Tierra Firme” forest in the Rio Negro Basin, Venezuela. Biotropica 15:185–189CrossRefGoogle Scholar
Rayachhetry, MB, Van, TK, Center, TD, Laroche, F (2001) Dry weight estimation of the aboveground components of Melaleuca quinquenervia trees in southern Florida. For Ecol Manag 142:281–290CrossRefGoogle Scholar
Rayamajhi, MB, Pratt, PD, Center, TD, Tipping, PW, Van, TK (2008) Aboveground biomass of an invasive tree melaleuca (Melaleuca quinquenervia) before and after herbivory by adventive and introduced natural enemies: a temporal case study in Florida. Weed Sci 56:451–456CrossRefGoogle Scholar
Rayamajhi, MB, Pratt, PD, Tipping, PW, Lake, E, Smith, M, Rohrig, E, Dray, FA, Center, TD (2016) Seasonal growth, biomass allocation, and invasive attributes manifested by Dioscorea bulbifera L. (air potato) plants generated from bulbils in Florida. Invasive Plant Sci Manag 9: 195–204CrossRefGoogle Scholar
Rayamajhi, MB, Rohrig, E, Leidi, J, Kerr, C, Salcedo, RE, Poffenberger, R, Smith, M, Lake, E, Dray, FA, Pratt, P, Tipping, P, Center, T (2019) Herbivory by the biocontrol agent Lilioceris cheni suppresses propagule production and smothering ability of the invasive vine Dioscorea bulbifera. Biol Control 130:1–8CrossRefGoogle Scholar
Sprugel, DG (1983) Correcting for bias in log-transformed allometric equations. Ecology 64:209–210CrossRefGoogle Scholar
Temesgen, H, Affleck, D, Poudel, K, Gray, A, Sessions, J (2015) A review of the challenges and opportunities in estimating above ground forest biomass using tree-level models. Scand J For Res 30:326–335Google Scholar
Tindall, HD (1993) Vegetables in the Tropics. London: MacMillan. 533 pGoogle Scholar
Vora, S (1988) Predicting biomass of five shrub species in northeastern California. J Range Manage 41:63–65CrossRefGoogle Scholar
Wheeler, GS, Pemberton, RW, Raz, L (2007) A biological control feasibility study of the invasive weed-air potato, Dioscorea bulbifera L. (Dioscoreaceae): an effort to increase biological control transparency and safety. Nat Areas J 27:269–279CrossRefGoogle Scholar
Yang, H, Wang, Z, Tan, H, Gao, Y (2017) Allometric models for estimating shrub biomass in desert grassland in northern China. Arid Land Res Manag 31:283–300CrossRefGoogle Scholar