Conservation implications of omitting narrow-ranging taxa from species distribution models, now and in the future

2014 ◽  
Vol 20 (11) ◽  
pp. 1307-1320 ◽  
Author(s):  
Philip J. Platts ◽  
Raquel A. Garcia ◽  
Christian Hof ◽  
Wendy Foden ◽  
Louis A. Hansen ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Conservation Implications ◽  
The Future

Using species distribution models to effectively conserve biodiversity into the future

Biodiversity ◽  
2008 ◽  
Vol 9 (3-4) ◽  
pp. 39-46 ◽  
Author(s):  
Heather M. Kharouba ◽  
Julie L. Nadeau ◽  
Eric Young ◽  
Jeremy T. Kerr
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
The Future

Combining physiological threshold knowledge to species distribution models is key to improving forecasts of the future niche for macroalgae

2014 ◽  
Vol 21 (4) ◽  
pp. 1422-1433 ◽  
Author(s):  
Brezo Martínez ◽  
Francisco Arenas ◽  
Alba Trilla ◽  
Rosa M. Viejo ◽  
Francisco Carreño
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
The Future

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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