scholarly journals Edge effect on the spatial distribution of trees in an Araucaria Rainforest fragment in Brazil

Rodriguésia ◽  
2018 ◽  
Vol 69 (4) ◽  
pp. 1937-1952
Author(s):  
Ângela Maria Klein Hentz ◽  
Ana Paula Dalla Corte ◽  
Carlos Roberto Sanquetta ◽  
Christopher Thomas Blum
Keyword(s):  
Spatial Distribution ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Kernel Density ◽  
Species Level ◽  
Ecological Groups ◽  
Climax Species ◽  
Forest Inventories ◽  
Edge Distance ◽  
Spearman’S Correlation

Abstract The objective of this research was to evaluate which species in the Brazilian Araucaria forest have its spatial distribution influenced by edge effects. We performed annual forest inventories inside two one-hectare plots, divided in 10 rectangular subplots, delimited by every 10 meters of edge distance. Each tree with at least 10 cm at DBH was identified to species level, and their allometric measurements and geographical coordinates were recorded considering the categories living, recruitment and mortality. We analyzed the correlation between the abundance of each species in each subplot and its distance by the Spearman's Correlation Coefficient and a Generalized Additive Model (GAM) with a Poisson distribution. We analyzed the distribution of some species and the ecological groups using a Kernel density model. We observed numerous pioneers and early secondary species with relationship with the edge distance, usually concentrated close to the edges. The late secondary/climax species are more evenly distributed in the plots, despite of some species, as Eugenia uniflora, are negatively affected by the edge. From these results, it is observed that some light demanding species can be favored to live close to the edges, even if some shadow tolerant species can inhabit this region as well.

scholarly journals Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 53
Author(s):  
Yves Staudt ◽  
Joël Wagner
Keyword(s):  
Random Forests ◽  
Goodness Of Fit ◽  
Generalized Additive Model ◽  
Mean Squared Error ◽  
Additive Model ◽  
Continuous Variables ◽  
Insurance Portfolio ◽  
Normal Transformation ◽  
Car Insurance ◽  
Log Normal

For calculating non-life insurance premiums, actuaries traditionally rely on separate severity and frequency models using covariates to explain the claims loss exposure. In this paper, we focus on the claim severity. First, we build two reference models, a generalized linear model and a generalized additive model, relying on a log-normal distribution of the severity and including the most significant factors. Thereby, we relate the continuous variables to the response in a nonlinear way. In the second step, we tune two random forest models, one for the claim severity and one for the log-transformed claim severity, where the latter requires a transformation of the predicted results. We compare the prediction performance of the different models using the relative error, the root mean squared error and the goodness-of-lift statistics in combination with goodness-of-fit statistics. In our application, we rely on a dataset of a Swiss collision insurance portfolio covering the loss exposure of the period from 2011 to 2015, and including observations from 81 309 settled claims with a total amount of CHF 184 mio. In the analysis, we use the data from 2011 to 2014 for training and from 2015 for testing. Our results indicate that the use of a log-normal transformation of the severity is not leading to performance gains with random forests. However, random forests with a log-normal transformation are the favorite choice for explaining right-skewed claims. Finally, when considering all indicators, we conclude that the generalized additive model has the best overall performance.

scholarly journals Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics

2021 ◽  
Vol 13 (1) ◽  
pp. 796-806
Author(s):  
Zhen Shuo ◽  
Zhang Jingyu ◽  
Zhang Zhengxiang ◽  
Zhao Jianjun
Keyword(s):  
Spatial Distribution ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Function Method ◽  
Kernel Density ◽  
Fire Occurrence ◽  
Distribution Characteristics ◽  
Fire Point ◽  
Spatial Distribution Characteristics ◽  
Grassland Fire

Abstract Understanding the risk of grassland fire occurrence associated with historical fire point events is critical for implementing effective management of grasslands. This may require a model to convert the fire point records into continuous spatial distribution data. Kernel density estimation (KDE) can be used to represent the spatial distribution of grassland fire occurrences and decrease the influences historical records in point format with inaccurate positions. The bandwidth is the most important parameter because it dominates the amount of variation in the estimation of KDE. In this study, the spatial distribution characteristic of the points was considered to determine the bandwidth of KDE with the Ripley’s K function method. With high, medium, and low concentration scenes of grassland fire points, kernel density surfaces were produced by using the kernel function with four bandwidth parameter selection methods. For acquiring the best maps, the estimated density surfaces were compared by mean integrated squared error methods. The results show that Ripley’s K function method is the best bandwidth selection method for mapping and analyzing the risk of grassland fire occurrence with the dependent or inaccurate point variable, considering the spatial distribution characteristics.

scholarly journals Generalized additive model with embedded variable selection for bankruptcy prediction: Prediction versus interpretation

2019 ◽  
Vol 7 (1) ◽  
pp. 1597956
Author(s):  
Carlos Valencia ◽  
Sergio Cabrales ◽  
Laura Garcia ◽  
Juan Ramirez ◽  
Diego Calderona ◽  
...  
Keyword(s):  
Variable Selection ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Bankruptcy Prediction ◽  
Selection For

scholarly journals Deoxygenation impacts on Baltic Sea cod: Dramatic declines in ecosystem services of an iconic keystone predator

AMBIO ◽  
2021 ◽  
Author(s):  
Alessandro Orio ◽  
Yvette Heimbrand ◽  
Karin Limburg
Keyword(s):  
Ecosystem Services ◽  
Baltic Sea ◽  
Gadus Morhua ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Maximum Length ◽  
The Baltic Sea ◽  
Hypoxic Zone ◽  
The Baltic ◽  
Baltic Cod

AbstractThe intensified expansion of the Baltic Sea’s hypoxic zone has been proposed as one reason for the current poor status of cod (Gadus morhua) in the Baltic Sea, with repercussions throughout the food web and on ecosystem services. We examined the links between increased hypoxic areas and the decline in maximum length of Baltic cod, a demographic proxy for services generation. We analysed the effect of different predictors on maximum length of Baltic cod during 1978–2014 using a generalized additive model. The extent of minimally suitable areas for cod (oxygen concentration ≥ 1 ml l−1) is the most important predictor of decreased cod maximum length. We also show, with simulations, the potential for Baltic cod to increase its maximum length if hypoxic areal extent is reduced to levels comparable to the beginning of the 1990s. We discuss our findings in relation to ecosystem services affected by the decrease of cod maximum length.

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