scholarly journals Development and Comparison of Species Distribution Models for Forest Inventories

2017 ◽  
Vol 6 (6) ◽  
pp. 176 ◽  
Author(s):  
Óscar Rodríguez de Rivera ◽  
Antonio López-Quílez
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Forest Inventories

scholarly journals SPATIAL SCALE EFFECTS OF SAMPLING ON THE INTERPOLATION OF SPECIES DISTRIBUTION MODELS IN THE SOUTHWESTERN AMAZON

2016 ◽  
Vol 40 (4) ◽  
pp. 617-625 ◽  
Author(s):  
Symone Maria de Melo Figueiredo ◽  
Eduardo Martins Venticinque ◽  
Evandro Orfanó Figueiredo
Keyword(s):  
Forest Management ◽  
Spatial Scale ◽  
Species Distribution ◽  
Tree Species ◽  
Species Distribution Models ◽  
Scale Effects ◽  
Projection Area ◽  
Distribution Models ◽  
Forest Inventories ◽  
Occurrence Data

ABSTRACT Knowledge of the geographical distribution of timber tree species in the Amazon is still scarce. This is especially true at the local level, thereby limiting natural resource management actions. Forest inventories are key sources of information on the occurrence of such species. However, areas with approved forest management plans are mostly located near access roads and the main industrial centers. The present study aimed to assess the spatial scale effects of forest inventories used as sources of occurrence data in the interpolation of potential species distribution models. The occurrence data of a group of six forest tree species were divided into four geographical areas during the modeling process. Several sampling schemes were then tested applying the maximum entropy algorithm, using the following predictor variables: elevation, slope, exposure, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). The results revealed that using occurrence data from only one geographical area with unique environmental characteristics increased both model overfitting to input data and omission error rates. The use of a diagonal systematic sampling scheme and lower threshold values led to improved model performance. Forest inventories may be used to predict areas with a high probability of species occurrence, provided they are located in forest management plan regions representative of the environmental range of the model projection area.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

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