scholarly journals Trade‐offs in covariate selection for species distribution models: a methodological comparison

Ecography ◽  
2019 ◽  
Vol 43 (1) ◽  
pp. 11-24 ◽  
Author(s):  
Stephanie J. Brodie ◽  
James T. Thorson ◽  
Gemma Carroll ◽  
Elliott L. Hazen ◽  
Steven Bograd ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Covariate Selection ◽  
Trade Offs ◽  
Selection For ◽  
Methodological Comparison

scholarly journals Feeding habitat quality and behavioral trade-offs in chimpanzees: a case for species distribution models

2016 ◽  
Vol 27 (4) ◽  
pp. 1004-1016 ◽  
Author(s):  
Steffen Foerster ◽  
Ying Zhong ◽  
Lilian Pintea ◽  
Carson M. Murray ◽  
Michael L. Wilson ◽  
...  
Keyword(s):  
Habitat Quality ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Trade Offs ◽  
Feeding Habitat

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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