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Old-fields (44, aged 1–15 years, from Czech Republic and Hungary) were sorted according to their soil moisture and nitrogen content into wet, mesic or dry, and nutrient poor, moderate or nutrient rich categories, resulting in 8 combinations (dry and nutrient rich fields were not present). The vegetation of old fields was sampled using phytosociological relevès. The changes in species cover data and importance of species trait categories were analysed in relation to three environmental factors, i.e., time since abandonment, soil moisture and total soil nitrogen using ordination, generalized linear models (GLM) and regression tree methods. Successional seres in the first 15 years after field abandonment were divergent. Species diversity significantly decreased with increasing site moisture and was highest in sites with moderate nitrogen content; while the relationship with time was not significant. Raunkiaer life forms and life strategies (sensu Grime) were generally the most predictive species traits considering species occurrence during the course of succession, the type of dispersal considering the different moisture status, and the ability to lateral spread considering the nutrient status of the old-fields. Most trends appeared in both parametric GLM and non-parametric regression tree analyses, several only in GLM. We consider regression trees to be a more convenient tool than GLM in cases such as ours with a rather small number of samples and robust character of data. Another advantage is that a hierarchy of species traits is taken into account. Thus, the occurrence of a species along an environmental gradient can be predicted if the species possesses a certain combination of traits.

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, L., J. H. Friedman, R. A. Olshen and C. J. Stone. 1984. Classification and Regression Trees . Wadsworth, Belmont, CA. Stone C. J. Classification

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Breiman, L., J. H. Friedman, R. Olshen, and C. J. Stone. 1984. Classification and Regression Trees . Chapman & Hall, New York. Stone C. J

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Three common terricolous Toninia species (Toninia opuntioides, T. physaroides and T. sedifolia) — formerly kept under Toninia coeruleonigricans — were investigated in the same locality for comparison of various substrate parameters of their microhabitats. Classification and regression trees (CART) were used to examine the niche preference of these species. Additionally, we also examined the predictability of the colonising species given the particular characteristics of a site. More than two hundred soil samples were analysed in the respect of eight soil parameters. The classification trees revealed that the different species prefer distinct substrate types, each of which was defined by combination of 3–4 environmental variables. Carbonate content, pH, hygroscopicity, soil depth and exposition seem to be the most important predictors of abundance.

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Spatial distribution modelling can be a useful tool for elaborating conservation strategies for tree species characterized by fragmented and sparse populations. We tested five statistical models—Support Vector Regression (SVR), Multivariate Adaptive Regression Splines (MARS), Gaussian processes with radial basis kernel functions (GP), Regression Tree Analysis (RTA) and Random Forests (RF)—for their predictive performances. To perform the evaluation, we applied these techniques to three tree species for which conservation measures should be elaborated and implemented: one Mediterranean species ( Quercus suber ) and two temperate species ( Ilex aquifolium and Taxus baccata ). Model evaluation was measured by MSE, Goodman-Kruskal and sensitivity statistics and map outputs based on the minimal predicted area criterion. All the models performed well, confirming the validity of this approach when dealing with species characterized by narrow and specialized niches and when adequate data (more than 40–50 samples) and environmental and climatic variables, recognized as important determinants of plant distribution patterns, are available. Based on the evaluation processes, RF resulted the most accurate algorithm thanks to bootstrap-resampling, trees averaging, randomization of predictors and smoother response surface.

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boosted regression trees. J. Animal Ecol. 77: 802–813. Hastie T. A working guide to boosted regression trees J. Animal Ecol

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optimistic induction. Technical Report CS-92-12 Dept. Comput. Sci., Vanderbilt Univ. Breiman, L., J. H. Friedman, R. A. Orshen and C. J. Stone. 1984. Classification and Regression Trees. Belmont CA. Wadsworth

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and regression tree analysis approach. PLoS ONE 2015; 10(3): e0121430. 29 Chiolero A, Faeh D, Paccaud F, et al. Consequences of smoking for body weight, body fat distribution

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„Rés a pajzson” – a pajzsmirigy modern képalkotó vizsgálata multidiszciplináris szemszögből

“Gap in the shield” – imaging of the thyroid gland from the multidisciplinary perspective

Orvosi Hetilap
Authors:
Péter Palásti
,
Tamás Zombori
,
László Kaiser
,
Sándor Magony
,
Flóra Kakuja
,
András Vörös
,
Zita Morvay
,
Zsigmond Tamás Kincses
,
András Palkó
, and
Zsuzsanna Fejes

–237. 19 Smayra T, Charara Z, Sleilaty G, et al. Classification and regression tree (CART) model of sonographic signs in predicting thyroid nodules malignancy. Eur J Radiol Open 2019; 6: 343

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pedotransfer functions for estimating soil bulk density using boosted regression trees . Soil Science Society of America Journal . 73 . ( 2 ) 485 – 493 . M artinez , G. , P achepsky , Y a

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