- More efficient estimation of plant biomass - 653 Journal of Vegetation Science 15: 653-660, 2004 © IAVS; Opulus Press Uppsala. Abstract Question: The optimal use of the point intercept method (PIM) for efficient estimation of plant biomass has not been addressed although PIM is a commonly used method in veg- etation analysis. In this study we compare results achieved using PIM at a range of efforts, we assess a method for calculating these results that are new with PIM and we provide a formula for planning the optimal use of PIM. Location: Northern Norway. Methods: We collected intercept data at a range of efforts, i.e. from one to 100 pins per 0.25 m 2 plots, on three plant growth forms in a mountain meadow. After collection of intercept data we clipped and weighed the plant biomass. The relation- ship between intercept frequency and weighed biomass (b) was estimated using both a weighted linear regression model (WLR) and an ordinary linear regression model (OLR). The accuracy of the estimate of biomass achieved by PIM at different efforts was assessed by running computer simulations at different pin densities. Results: The relationship between intercept frequency and weighed biomass (b) was far better estimated using WLR compared to the normally used OLR. Efforts above 10 pins per 0.25 m 2 plot had a negligible effect on the accuracy of the estimate of biomass achieved by PIM whereas the number of plots had a strong effect. Moreover, for a given level of accu- racy, the required number of plots varied depending on plant growth form. We achieved similar results to that of the compu- ter simulations when applying our WLR based formula. Conclusion: This study shows that PIM can be applied more efficiently than was done in previous studies for the purpose of plant biomass estimation, where several plots should be ana- lysed but at considerably less effort per plot. Moreover, WLR rather than OLR should be applied when estimating biomass from intercept frequency. The formula we have deduced is a useful tool for planning plant biomass analysis with PIM. Keywords: Bistorta vivipara; Ericoid; Graminoid; Point inter- cept method; Rhinanthus minor; Weighted linear regression. Nomenclature: Lid & Lid (1994). Abbreviations: BM = Biomass; CV = Coefficient of varia- tion; IF = Intercept frequency; PIM = Point intercept method; OLR = Ordinary linear regression model; WLR = weighted linear regression model. Introduction Biomass is the preferred measure when assessing plant species abundance, species richness and species evenness (Guo & Rundel 1997; Chiarucci et al. 1999, but see Mason et al. 2002). Yet, in certain low and open vegetation types, cover analysis can be used as an alter- native to biomass measurement (Röttgermann et al. 2000). Ecosystem research and monitoring often de- mand vegetation analysis over large areas (Freckleton & Watkinson 2001), increasing the demand for unbiased and efficient sampling designs (Yoccoz et al. 2001). Still, there is no technique that can replace the more detailed information gathered from ground analysis in the measure of biomass (Wyatt 2000). Resource limita- tions for analysis often leave the researcher in a di- lemma of choosing between a large number of fast and inaccurate measurements, enabling poor accuracy but large area estimates of biomass, and a small number of slow and accurate measurements, enabling good accu- racy but small area estimates that must be extrapolated. Obviously, neither alternative is adequate, and there is clearly a need for methods that reduce tedious biomass analysis in the field. In this study, we ask if the efficiency of the point intercept method (PIM), a recommended method for estimation of biomass, can be improved. PIM yields the number of contacts between plants and a pin passed through the vegetation, i.e. the number of intercepts, at a large number of positions (Levy & Madden 1933; Goodall 1952). Because the number of intercepts is proportional to the amount of biomass, PIM gives good estimates of above-ground plant biomass (Jonasson 1988), that is, as long as different plant growth forms are analysed sepa- rately (Frank & McNaughton 1990). Additionally, the observer bias of PIM is low and it is non-destructive, which makes it a superior method in vegetation analysis (Goodall 1952). The amount of effort necessary to reach consistent results by PIM in the estimation of the number of plant species (Stampfli 1991) and in the estimation of biomass in relation to the plot size (Jonasson 1988) has been addressed. However, whether the time consumed per plot (i.e. pin density) can be reduced and how this is More efficient estimation of plant biomass Bråthen, Kari Anne 1* & Hagberg, Oskar 2 1 Institute of Biology, University of Tromsø, N-9037 Tromsø, Norway; 2 Centre for Mathematical Sciences, Lund University, Box 118, S-221 00 Lund, Sweden; E-mail oskar@maths.lth.se; * Corresponding author; Fax + 4777646333; E-mail kari.brathen@ib.uit.no