Integrating models of relative abundance of species with the dry-weight-rank method for the botanical analysis of forest understorey vegetation

2002 ◽  
Vol 57 (2) ◽  
pp. 171-183 ◽  
Author(s):  
E. Gonzalez-Estrada ◽  
R. H. Fawcett ◽  
M. Herrero
Author(s):  
I.M. Ritchie ◽  
C.C. Boswell ◽  
A.M. Badland

HERBACE DISSECTION is the process in which samples of herbage cut from trials are separated by hand into component species. Heavy reliance is placed on herbage dissection as an analytical tool ,in New Zealand, and in the four botanical analysis laboratories in the Research Division of the Ministry of Agriculture and Fisheries about 20 000 samples are analysed each year. In the laboratory a representative subsample is taken by a rigorous quartering procedure until approximately 400 pieces of herbage remain. Each leaf fragment is then identified to species level or groups of these as appropriate. The fractions are then dried and the composition calculated on a percentage dry weight basis. The accuracy of the analyses of these laboratories has been monitored by a system of interchanging herbage dissection samples between them. From this, the need to separate subsampling errors from problems of plant identification was, appreciated and some of this work is described here.


2004 ◽  
Vol 15 (4) ◽  
pp. 437-448 ◽  
Author(s):  
Tonje Økland ◽  
Vegar Bakkestuen ◽  
Rune Halvorsen Økland ◽  
Odd Eilertsen

2020 ◽  
Author(s):  
Bechu Kumar Vinwar Yadav ◽  
Arko Lucieer ◽  
Gregory J. Jordan ◽  
Susan C. Baker

Abstract Background: Forest understorey structure is an important component of forest ecosystems that affects forest-dwelling species, nutrient cycling, fire behaviour, biodiversity, and regeneration capacity. Mapping the structure of forest understorey vegetation with field surveys or high-resolution LiDAR data is costly. We tested whether landscape topography and underlying geology could predict the understorey structure of a 19 km2 area of wet eucalypt primary forest located at the Warra Long Term Ecological Research Supersite, Tasmania, Australia. In this study, we used random forest regressions based on twelve topographic attributes derived from digital terrain models (DTMs) at various resolutions and a geology variable to predict the densities of three understorey layers compared to density estimates from a high resolution (28.66 points/m2) LiDAR survey. Results: We predicted the vegetation density of three canopy strata with a high degree of accuracy (validation root mean square error ranged from 8.97% to 13.69%). 30 m resolution DTMs provided greater predictive accuracy than DTMs with higher spatial resolution. Variable importance depended on spatial resolutions and canopy strata layers, but among the predictor variables, geology generally produced the highest predictive importance followed by solar radiation. Topographic position index, aspect, and SAGA wetness index had moderate importance. Conclusions: This study demonstrates that geological and topographic attributes can provide useful predictions of understorey vegetation structure in a primary forest. Given the good performance of 30 m resolution, the predictive power of the models could be tested on a larger geographical area using lower density LiDAR point clouds. This study should help in assessing fuel loads, carbon stores, biomass, and biological diversity, and could be useful for foresters and ecologists contributing to the planning of sustainable forest management and biodiversity conservation.


2015 ◽  
Vol 103 (6) ◽  
pp. 1610-1620 ◽  
Author(s):  
Bright B. Kumordzi ◽  
Francesco de Bello ◽  
Grégoire T. Freschet ◽  
Yoann Le Bagousse-Pinguet ◽  
Jan Lepš ◽  
...  

1979 ◽  
Vol 27 (6) ◽  
pp. 725 ◽  
Author(s):  
HTL Stewart ◽  
DW Flinn ◽  
BC Aeberli

Eleven trees of Eucalyptus muellerana and 10 trees both of E. agglomerata and of E. sieberi growing in an uneven-aged mixed sclerophyll forest on duplex granitoid soils in eastern Victoria were felled, measured. separated into branch and stem components, sampled and weighed. Understorey vegetation and litter were also sampled for dry weight determination. Both linear and allometric regressions were developed for each species to predict branch and stem component dry weights from branch and tree dimensions. The predicted component weights for all branches on each tree were summed to estimate crown component dry weights, and regressions were then fitted for these crown component dry weights as functions of tree dimensions. Land area estimates of above-ground tree biomass were made by measuring tree diameters on sample plots. applying the appropriate regressions relating stem and crown component dry weights to tree diameter, and summing the predicted weights for each plot. The above-ground biomass of the forest ecosystem. which had a tree density of 123 stems per ha, was estimated to be 344.100 kg ha-1 of which 94.6% was in the forest overstorey. The proportions of each tree component in the overstorey were stem wood 60.1%, stem bark 15.8%. branch wood 16.5%, branch bark 3.9%, twigs 2.0%, and leaves 1.7%.


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