scholarly journals The devil is in the detail: unstable response functions in species distribution models challenge bulk ensemble modelling

2015 ◽  
Vol 25 (1) ◽  
pp. 26-35 ◽  
Author(s):  
Henrik Hannemann ◽  
Katherine J. Willis ◽  
Marc Macias-Fauria
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Response Functions ◽  
Distribution Models ◽  
Ensemble Modelling ◽  
The Devil

scholarly journals Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models

Ecography ◽  
2020 ◽  
Vol 43 (4) ◽  
pp. 549-558 ◽  
Author(s):  
Tianxiao Hao ◽  
Jane Elith ◽  
José J. Lahoz‐Monfort ◽  
Gurutzeta Guillera‐Arroita
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Predictive Performance ◽  
Distribution Models ◽  
Ensemble Modelling

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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