scholarly journals Species Diversity and Community Assembly of Cladocera in the Sand Ponds of the Ulan Buh Desert, Inner Mongolia of China

Diversity ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 502
Author(s):  
Yang-Liang Gu ◽  
Qi Huang ◽  
Lei Xu ◽  
Eric Zeus Rizo ◽  
Miguel Alonso ◽  
...  
Keyword(s):  
Species Richness ◽  
Species Diversity ◽  
Community Assembly ◽  
Inner Mongolia ◽  
Regression Tree ◽  
Environmental Variable ◽  
Classification And Regression Tree ◽  
Tree Model ◽  
Environmental Selection ◽  
Ulan Buh Desert

In deserts, pond cladocerans suffer harsh conditions like low and erratic rainfall, high evaporation, and highly variable salinity, and they have limited species richness. The limited species can take advantage of ephippia or resting eggs for being dispersed with winds in such habitats. Thus, environmental selection is assumed to play a major role in community assembly, especially at a fine spatial scale. Located in Inner Mongolia, the Ulan Buh desert has plenty of temporary water bodies and a few permanent lakes filled by groundwater. To determine species diversity and the role of environmental selection in community assembly in such a harsh environment, we sampled 37 sand ponds in June 2012. Fourteen species of Cladocera were found in total, including six pelagic species, eight littoral species, and two benthic species. These cladocerans were mainly temperate and cosmopolitan fauna. Our classification and regression tree model showed that conductivity, dissolved oxygen, and pH were the main factors correlated with species richness in the sand ponds. Spatial analysis using a PCNM model demonstrated a broad-scale spatial structure in the cladoceran communities. Conductivity was the most significant environmental variable explaining cladoceran community variation. Two species, Moina cf. brachiata and Ceriodaphnia reticulata occurred commonly, with an overlap at intermediate conductivity. Our results, therefore, support that environmental selection plays a major role in structuring cladoceran communities in deserts.

scholarly journals Modeling and Testing Landslide Hazard Using Decision Tree

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mutasem Sh. Alkhasawneh ◽  
Umi Kalthum Ngah ◽  
Lea Tien Tay ◽  
Nor Ashidi Mat Isa ◽  
Mohammad Subhi Al-Batah
Keyword(s):  
Decision Tree ◽  
Surface Area ◽  
Slope Angle ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Slope Aspect ◽  
Tree Model ◽  
Texture Surface ◽  
Data Set ◽  
Exhaustive Chaid

This paper proposes a decision tree model for specifying the importance of 21 factors causing the landslides in a wide area of Penang Island, Malaysia. These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. Four models were created using Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CRT), and Quick-Unbiased-Efficient Statistical Tree (QUEST). Twenty-one factors were extracted using digital elevation models (DEMs) and then used as input variables for the models. A data set of 137570 samples was selected for each variable in the analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. 10-fold cross-validation was employed for testing the models. The highest accuracy was achieved using Exhaustive CHAID (82.0%) compared to CHAID (81.9%), CRT (75.6%), and QUEST (74.0%) model. Across the four models, five factors were identified as most important factors which are slope angle, distance from drainage, surface area, slope aspect, and cross curvature.

scholarly journals A diagnostic strategy for Parkinsonian syndromes using quantitative indices of DAT SPECT and MIBG scintigraphy: an investigation using the classification and regression tree analysis

2021 ◽  
Author(s):  
Yu Iwabuchi ◽  
Masashi Kameyama ◽  
Yohji Matsusaka ◽  
Hidetoshi Narimatsu ◽  
Masahiro Hashimoto ◽  
...  
Keyword(s):  
Decision Tree ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Mibg Scintigraphy ◽  
Tree Model ◽  
Quantitative Indices ◽  
Cart Analysis ◽  
Parkinsonian Syndromes ◽  
Dat Spect ◽  
Classification And Regression

Abstract Purpose We aimed to evaluate the diagnostic performances of quantitative indices obtained from dopamine transporter (DAT) single-photon emission computed tomography (SPECT) and 123I-metaiodobenzylguanidine (MIBG) scintigraphy for Parkinsonian syndromes (PS) using the classification and regression tree (CART) analysis. Methods We retrospectively enrolled 216 patients with or without PS, including 80 without PS (NPS) and 136 with PS [90 Parkinson’s disease (PD), 21 dementia with Lewy bodies (DLB), 16 progressive supranuclear palsy (PSP), and 9 multiple system atrophy (MSA). The striatal binding ratio (SBR), putamen-to-caudate ratio (PCR), and asymmetry index (AI) were calculated using DAT SPECT. The heart-to-mediastinum uptake ratio (H/M) based on the early (H/M [Early]) and delayed (H/M [Delay]) images and cardiac washout rate (WR) were calculated from MIBG scintigraphy. The CART analysis was used to establish a diagnostic decision tree model for differentiating PS based on these quantitative indices. Results The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 87.5, 96.3, 93.3, 92.9, and 93.1 for NPS; 91.1, 78.6, 75.2, 92.5, and 83.8 for PD; 57.1, 95.9, 60.0, 95.4, and 92.1 for DLB; and 50.0, 98.0, 66.7, 96.1, and 94.4 for PSP, respectively. The PCR, WR, H/M (Delay), and SBR indices played important roles in the optimal decision tree model, and their feature importance was 0.61, 0.22, 0.11, and 0.05, respectively. Conclusion The quantitative indices showed high diagnostic performances in differentiating NPS, PD, DLB, and PSP, but not MSA. Our findings provide useful guidance on how to apply these quantitative indices in clinical practice.

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