Exploring the relative importance of biotic and abiotic factors that alter the self-thinning rule: Insights from individual-based modelling and machine-learning

2019 ◽  
Vol 397 ◽  
pp. 16-24 ◽  
Author(s):  
Ping Ma ◽  
Xiao-Hui Han ◽  
Yue Lin ◽  
John Moore ◽  
Yao-Xin Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Abiotic Factors ◽  
The Self ◽  
Relative Importance ◽  
Biotic And Abiotic Factors

The relative importance of biotic and abiotic factors influencing aspen recruitment in Arizona

2019 ◽  
Vol 441 ◽  
pp. 32-41 ◽  
Author(s):  
Matthew J. Clement ◽  
Larisa E. Harding ◽  
Richard W. Lucas ◽  
Esther S. Rubin
Keyword(s):  
Abiotic Factors ◽  
Relative Importance ◽  
Biotic And Abiotic Factors ◽  
Factors Influencing

scholarly journals Assessing Biotic and Abiotic Effects on Biodiversity Index Using Machine Learning

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 461
Author(s):  
Mahmoud Bayat ◽  
Harold Burkhart ◽  
Manouchehr Namiranian ◽  
Seyedeh Kosar Hamidi ◽  
Sahar Heidari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Forest Ecosystems ◽  
Abiotic Factors ◽  
Basal Area ◽  
Tree Diversity ◽  
Additive Models ◽  
Slope Aspect ◽  
Biotic And Abiotic Factors ◽  
The Relationship

Forest ecosystems play multiple important roles in meeting the habitat needs of different organisms and providing a variety of services to humans. Biodiversity is one of the structural features in dynamic and complex forest ecosystems. One of the most challenging issues in assessing forest ecosystems is understanding the relationship between biodiversity and environmental factors. The aim of this study was to investigate the effect of biotic and abiotic factors on tree diversity of Hyrcanian forests in northern Iran. For this purpose, we analyzed tree diversity in 8 forest sites in different locations from east to west of the Caspian Sea. 15,988 trees were measured in 655 circular permanent sample plots (0.1 ha). A combination of machine learning methods was used for modeling and investigating the relationship between tree diversity and biotic and abiotic factors. Machine learning models included generalized additive models (GAMs), support vector machine (SVM), random forest (RF) and K-nearest–neighbor (KNN). To determine the most important factors related to tree diversity we used from variables such as the average diameter at breast height (DBH) in the plot, basal area in largest trees (BAL), basal area (BA), number of trees per hectare, tree species, slope, aspect and elevation. A comparison of RMSEs, relative RMSEs, and the coefficients of determination of the different methods, showed that the random forest (RF) method resulted in the best models among all those tested. Based on the results of the RF method, elevation, BA and BAL were recognized as the most influential factors defining variation of tree diversity.

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