Niche shift in four non-native estrildid finches and implications for species distribution models

Ibis ◽  
2014 ◽  
Vol 157 (1) ◽  
pp. 75-90 ◽  
Author(s):  
Darius Stiels ◽  
Bianca Gaißer ◽  
Kathrin Schidelko ◽  
Jan O. Engler ◽  
Dennis Rödder
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Niche Shift ◽  
Distribution Models

The effect of pseudo-absence selection method on transferability of species distribution models in the context of non-adaptive niche shift

2018 ◽  
Vol 388 ◽  
pp. 1-9 ◽  
Author(s):  
Wanwan Liang ◽  
Monica Papeş ◽  
Liem Tran ◽  
Jerome Grant ◽  
Robert Washington-Allen ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Selection Method ◽  
Niche Shift ◽  
Distribution Models

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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