scholarly journals Phytosociological overview of the Fagus and Corylus forests in Albania

2020 ◽  
Vol 1 ◽  
pp. 175-189
Author(s):  
Giuliano Fanelli ◽  
Petrit Hoda ◽  
Mersin Mersinllari ◽  
Ermelinda Mahmutaj ◽  
Fabio Attorre ◽  
...  
Keyword(s):  
Random Forest ◽  
Fagus Sylvatica ◽  
Potential Distribution ◽  
Corylus Avellana ◽  
Minimum Variance ◽  
Ecological Classification ◽  
Forest Types ◽  
Ostrya Carpinifolia ◽  
High Diversity

Aim: The aim of this study is to analyze the mesophilous forests of Albania including Fagus sylvatica and submontane Corylus avellana forests. Mesophilous Albanian forests are poorly known and were not included in the recent syntaxonomic revisions at the European scale. Study area: Albania. Methods: We used a dataset of 284 published and unpublished relevés. They were classified using the Ward’s minimum variance. NMDS ordination was conducted, with over-laying of climatic and geological variables, to analyze the ecological gradients along which these forests develop and segregate. Random Forest was used to define the potential distribution of the identified forest groups in Albania. Results: The study identified seven groups of forests in Albania: Corylus avellana forests, Ostrya carpinifolia-Fagus sylvatica forests, lower montane mesophytic Fagus sylvatica forests, middle montane mesophytic Fagus sylvatica forests, middle montane basiphytic Fagus sylvatica forests, upper montane basiphytic Fagus sylvatica forests, upper montane acidophytic Fagus sylvatica forests. These can be grouped into four main types: Corylus avellana and Ostrya carpinifolia-Fagus sylvatica forests, thermo-basiphytic Fagus sylvatica forest, meso-basiphytic Fagus sylvatica forest and acidophytic Fagus sylvatica forests. This scheme corresponds to the ecological classification recently proposed in a European revision for Fagus sylvatica forests Conclusion: Our study supports an ecological classification of mesophilous forests of Albania at the level of suballiance. Analysis is still preliminary at the level of association, but it shows a high diversity of forest types. Taxonomic reference: Euro+Med PlantBase (http://ww2.bgbm.org/EuroPlusMed/) [accessed 25 Novemeber 2019]. Syntaxonomic references: Mucina et al. (2016) for alliances, orders and classes; Willner et al. (2017) for suballiances.

scholarly journals Environmental DNA gives comparable results to morphology-based indices of macroinvertebrates in a large-scale ecological assessment

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257510
Author(s):  
Jeanine Brantschen ◽  
Rosetta C. Blackman ◽  
Jean-Claude Walser ◽  
Florian Altermatt
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Community Composition ◽  
Large Scale ◽  
Anthropogenic Activities ◽  
Environmental Dna ◽  
Biotic Index ◽  
Ecological Classification ◽  
Indicator Taxa

Anthropogenic activities are changing the state of ecosystems worldwide, affecting community composition and often resulting in loss of biodiversity. Rivers are among the most impacted ecosystems. Recording their current state with regular biomonitoring is important to assess the future trajectory of biodiversity. Traditional monitoring methods for ecological assessments are costly and time-intensive. Here, we compared monitoring of macroinvertebrates based on environmental DNA (eDNA) sampling with monitoring based on traditional kick-net sampling to assess biodiversity patterns at 92 river sites covering all major Swiss river catchments. From the kick-net community data, a biotic index (IBCH) based on 145 indicator taxa had been established. The index was matched by the taxonomically annotated eDNA data by using a machine learning approach. Our comparison of diversity patterns only uses the zero-radius Operational Taxonomic Units assigned to the indicator taxa. Overall, we found a strong congruence between both methods for the assessment of the total indicator community composition (gamma diversity). However, when assessing biodiversity at the site level (alpha diversity), the methods were less consistent and gave complementary data on composition. Specifically, environmental DNA retrieved significantly fewer indicator taxa per site than the kick-net approach. Importantly, however, the subsequent ecological classification of rivers based on the detected indicators resulted in similar biotic index scores for the kick-net and the eDNA data that was classified using a random forest approach. The majority of the predictions (72%) from the random forest classification resulted in the same river status categories as the kick-net approach. Thus, environmental DNA validly detected indicator communities and, combined with machine learning, provided reliable classifications of the ecological state of rivers. Overall, while environmental DNA gives complementary data on the macroinvertebrate community composition compared to the kick-net approach, the subsequently calculated indices for the ecological classification of river sites are nevertheless directly comparable and consistent.

scholarly journals Classification of Germination Images of Pear Pollen Using Random Forest and Convolution Neural Network Models

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 45993-45999
Author(s):  
Ung Yang ◽  
Seungwon Oh ◽  
Seung Gon Wi ◽  
Bok-Rye Lee ◽  
Sang-Hyun Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Network Models ◽  
Convolution Neural Network ◽  
Neural Network Models

Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest

2021 ◽  
Author(s):  
Balajee Alphonse ◽  
Venkatesan Rajagopal ◽  
Sudhakar Sengan ◽  
Kousalya Kittusamy ◽  
Amudha Kandasamy ◽  
...  
Keyword(s):  
Random Forest ◽  
Time Domain ◽  
Multi Class Classification

Classifying and Mapping Potential Distribution of Forest Types Using a Finite Mixture Model

2012 ◽  
Vol 49 (3) ◽  
pp. 313-335 ◽  
Author(s):  
Fabio Attorre ◽  
Fabio Francesconi ◽  
Michele De Sanctis ◽  
Marco Alfò ◽  
Francesca Martella ◽  
...  
Keyword(s):  
Mixture Model ◽  
Potential Distribution ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Forest Types

A data mining approach to the diagnosis of failure modes for two serial fastened sandwich composite plates

2016 ◽  
Vol 51 (20) ◽  
pp. 2853-2862 ◽  
Author(s):  
Serkan Ballı
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Failure Modes ◽  
Composite Plates ◽  
Study Data ◽  
Sandwich Composite ◽  
Support Vector ◽  
Geometrical Parameters ◽  
Mining Methods

The aim of this study is to diagnose and classify the failure modes for two serial fastened sandwich composite plates using data mining techniques. The composite material used in the study was manufactured using glass fiber reinforced layer and aluminum sheets. Obtained results of previous experimental study for sandwich composite plates, which were mechanically fastened with two serial pins or bolts were used for classification of failure modes. Furthermore, experimental data from previous study consists of different geometrical parameters for various applied preload moments as 0 (pinned), 2, 3, 4, and 5 Nm (bolted). In this study, data mining methods were applied by using these geometrical parameters and pinned/bolted joint configurations. Therefore, three geometrical parameters and 100 test data were used for classification by utilizing support vector machine, Naive Bayes, K-Nearest Neighbors, Logistic Regression, and Random Forest methods. According to experiments, Random Forest method achieved better results than others and it was appropriate for diagnosing and classification of the failure modes. Performances of all data mining methods used were discussed in terms of accuracy and error ratios.

Sign in / Sign up

Export Citation Format

Share Document