Scaling-up individual-level allometric equations to predict stand-level fuel loading in Mediterranean shrublands
Allometric biomass equations developed for individual shrubs can be applied to estimate shrubland fuels from measurements of cover and average height by species.
Shrubs are a major component of surface fuels in many fire-prone ecosystems. Shrub fuel loading is often estimated by “double sampling”, where data from a destructive vegetation survey is used to model vegetation-fuel relationships (development phase), and these relationships are then applied to vegetation data from a second survey (application phase). Vegetation-fuel relationships can be modeled at different levels of vegetation detail, from individual plants to stands, but the increased effort of detailed measurements may compromise large-scale applications.
To facilitate fuel loading assessments in Mediterranean shrublands, we present and test a novel method to estimate stand-level shrub loading that consists in applying individual-level allometric equations to vegetation plot data collected by measuring the percent cover and mean height of each shrub species.
We used individual-level data (i.e., plant dimensions and dry weights) to develop allometric equations for total and fine (leaves and branches < 6 mm) biomass of 26 Mediterranean shrub species. We then evaluated the accuracy and precision of the proposed method in comparison to an approach assuming constant bulk density, using data from 131 vegetation plots and taking as benchmark stand-level loading estimates derived by aggregation of individual biomass allometric estimates. A second set of 13 plots was used to quantify the additional error derived from visual estimation of species mean height and cover.
The performance of species-specific models was acceptable in estimating total and fine biomass at the individual level. When based on species mean height and cover data, stand-level fuel loading estimates calculated using the proposed method had a better precision and accuracy than those obtained using bulk density values (− 4 vs. + 39% in relative bias; 10 vs. 40% in relative MAE). Visual estimation of species mean height and percent cover led to 10 and 16% increase in MAE for species loading estimates of total and fine fuels, respectively, with respect to estimates obtained without this source of error.
Our approach to estimate shrub loading allows combining fast species-level vegetation sampling with the flexibility of individual-level allometries to model to size-related variations of bulk density.
KeywordsBulk density Fine fuels Fuel loading Fuel mapping Vegetation sampling
We are grateful to AI Ríos, M Sala, JM Blanco, R Vila and the Cos d’Agents rurals and GEPIF (Generalitat de Catalunya) for their help in clipping plants and field measurements. Some species’ datasets were kindly provided by Lluís Coll (UdL), Beatriz Duguy (UB), and Joan Romanyà (UB).
This work was supported by the Conselleria d’Agricultura de la Generalitat de Catalunya and the research projects CGL2017-89149-C2-2-R and RTI2018-098778-B-I00 (Spanish Ministry of Economy and Competitiveness) and by a Spanish “Ramon y Cajal” fellowship to M.D.C (RYC-2012-11109).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Blanco-Oyonarte P, Navarro-Cerrillo RM (2003) Aboveground phytomass models for major species in shrub ecosystems of western Andalusia. For Syst 12:47–55Google Scholar
- Carreras J, Ferré A, Vigo J (2016) Manual dels hàbitats de Catalunya: catàleg dels hàbitats naturals reconeguts en el territori català d'acord amb els criteris establerts pel CORINE biotopes manual de la Unió Europea. Vol. IV. ISBN 9788439395553. Pp. 309Google Scholar
- Castro I, Casado MÁ, Ramírez-Sanz L et al (1996) Funciones de estimación de la biomasa aérea de varias especies del matorral mediterráneo del centro de la península Ibérica. Orsis Org i Sist 11:107–116Google Scholar
- Catchpole WR, Wheeler CJ (1992) Review estimating plant biomass : a review of techniques. Aust J Ecol 17:121–131. https://doi.org/10.1111/j.1442-9993.1992.tb00790.x CrossRefGoogle Scholar
- De Cáceres M, Casals P, Gabriel E, Castro X (2019) Package ‘medfuels’ and data set of shrub individual measurements collected in Catalonia, Spain. Version v1.0. Zenodo. https://doi.org/10.5281/zenodo.3356777
- Fernandes P, Loureiro C, Botelho H et al (2002) Avaliação Indirecta da Carga de Combustível em Pinhal Bravo. Silva Lusit 10:73–90Google Scholar
- McCullagh P, Nelder JA (1989) Generalized linear models (monographs on statistics and applied probability 37). Chapman Hall, LondonGoogle Scholar
- Ottmar RD, Sandberg DV, Riccardi CL, Prichard SJ (2007) An overview of the fuel characteristic classification system - quantifying, classifying , and creating fuelbeds for resource planning. Can J For Res 37:2383–2393. https://doi.org/10.1139/X07-077 This article is one of a selection of papers published in the Special Forum on the Fuel Characteristic Classification SystemCrossRefGoogle Scholar
- Papió C, Trabaud L (1991) Comparative study of the aerial structure of five shrubs of Mediterranean shrublands. For Sci 37:146–159Google Scholar
- Pyne S (1984) Introduction to wildland fire management in the United States. Wiley-Interscience, New YorkGoogle Scholar
- Ruffault J, Martin-StPaul N, Pimont F, Dupuy JL (2018) How well do meteorological drought indices predict live fuel moisture content (LFMC)? An assessment for wildfire research and operations in Mediterranean ecosystems. Agric For Meteorol 262:391–401. https://doi.org/10.1016/j.agrformet.2018.07.031 CrossRefGoogle Scholar
- Villanueva JA (2004) Tercer inventario forestal nacional (1997–2007). Ministerio de Medio Ambiente y Medio Rural. ICONA, MadridGoogle Scholar