1,721,258 research outputs found
Applied Vegetation Science in 2010: new opportunities for the vegetation scientists
Full text linkFil: Chiarucci, Alessandro. University of Siena. Department of
Environmental Science G. Sarfatti. Biodiversity
and Conservation Network; ItaliaFil: Pärtel, Meelis. University of Tartu. Institute of Ecology
and Earth Sciences; EstoniaFil: Diaz, Sandra Myrna. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Wilson, J. Batow. University of Otago. Botany Department,
; Nueva Zeland
To sample or not to sample? That is the question ... for the vegetation scientist
No full textLÁJER (2007) raised the problem of using a non-random sample for statistical testing of plant community data. He argued that this violates basic assumptions of the tests, resulting thus in non-significant results. However, a huge part of present-day knowledge of vegetation science is still based on non-random, preferentially collected data of plant communities. I argue that, given the inherent limits of preferential sampling, a change of approach is now necessary, with the adoption of sampling based on random principles seeming the obvious choice. However, a complete transition to random-based sampling designs in vegetation science is limited by the yet undefined nature of plant communities and by the still diffused opinion that plant communities have a discrete nature. Randomly searching for such entities is almost impossible, given their dependence on scale of observation, plot size and shape, and the need for finding well-defined types. I conclude that the only way to solve this conundrum is to consider and study plant communities as operational units. If the limits of the plant communities are defined operationally, they can be investigated using proper sampling techniques and the collected data analyzed using adequate statistical tools
Estimating species richness: still a long way off!
No full textXu et al., in this issue of the Journal of Vegetation Science, compare several species richness estimators. All the non-parametric estimators, such as Chao and jackknife estimators, underestimated the true number, whereas all the area-based models, based on species–area curves, overestimated it. No reliable method yet exists to predict the number of species in an area that is appreciably larger than the one(s) sampled
Serpentine: The Evolution and Ecology of a Model System . Edited by Susan Harrison and Nishanta Rajakaruna . Berkeley (California): University of California Press. $70.00. xiv + 446 p.; ill.; species and subject indexes. ISBN: 978-0-520-26835-7. 2011.
No full textThe nature of vegetation science
Full text linkFil: Partel, Meelis. University of Tartu. Institute of Ecology
and Earth Sciences; EstoniaFil: Chiarucci, Alessandro. University of
Siena. Department of
Environmental Science ‘‘G. Sarfatti’’. BIOCONNET, Biodiversity
and Conservation Network; ItaliaFil: Diaz, Sandra Myrna. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Wilson, J. Bastow. University of Otago. Botany Department
; Nueva Zeland
The original collectopns of Nannizzi Arturo (1877-1961) in the herbarium Universitatis Senensis (Siena)
No full textThe nature, size and actual state of conservation of the original mycological collection of Arturo Nannizzi (1877-1961), eminent Siena mycologist of the Attilio Tassi (1820-1905) school, are herewith illustrated. The complete autoptic material of which Arturo Nannizzi described many new taxonomic units is listed. Such material needs to be typified and this essay shall represent a useful basis for those taxonomists interested in these systematic groups
Evaluation and monitoring of the flora in a nature reserve by estimation methods
No full textWe tested the use of non-parametric estimators of species richness to evaluate the flora of a relatively large (431 ha) nature reserve, using a sampling area much lower than that used in previous studies. Different estimation methods were applied to floristic data obtained from 50 random plots: the number of observed species, the extrapolated accumulation curves based on the Michaelis–Menten model and the non-parametric estimators based on incidence data (Chao2, first-order Jackknife, second-order Jackknife and bootstrap). To test the performance of the estimators, five data sets were created on the basis of life-forms. The estimates were compared with reference values obtained by traditional floristic and vegetation sampling. The power of the different estimation methods could not definitively be determined, but the first- and second-order Jackknives seem to be the most precise. Although total species richness was underestimated, the sample-based approach provided accurate information for quantitative comparison of time series of data related to ecological changes, vegetation dynamics and environmental changes. This sample-based data included basic statistics on species richness and species frequency distributions as well as the life-form spectrum, at the plot and the whole site scales
Invasive species, management for conservation and remote sensing
Full text linkFil: Bastow, Wilson J.. University of Otago. Botany Department, ; Nueva ZelandaFil: Chiarucci, Alessandro. University of Siena. Department of Environmental Science G. Sarfatti; ItaliaFil: Diaz, Sandra Myrna. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: White, Peter S.. University of North Carolina. Department of Biology; Estados Unido
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