Species distribution models for Peruvian plantcutter improve with consideration of biotic interactions

2018 ◽  
Vol 49 (3) ◽  
pp. jav-01617 ◽  
Author(s):  
P. Joser Atauchi ◽  
A. Townsend Peterson ◽  
Jeremy Flanagan
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Biotic Interactions ◽  
Distribution Models

scholarly journals Accounting for biotic interactions through alpha‐diversity constraints in stacked species distribution models

2017 ◽  
Vol 8 (9) ◽  
pp. 1092-1102 ◽  
Author(s):  
Yoni Gavish ◽  
Charles J. Marsh ◽  
Mathias Kuemmerlen ◽  
Stefan Stoll ◽  
Peter Haase ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Biotic Interactions ◽  
Alpha Diversity ◽  
Distribution Models

scholarly journals Improving species distribution models using biotic interactions: a case study of parasites, pollinators and plants

Ecography ◽  
2012 ◽  
Vol 36 (6) ◽  
pp. 649-656 ◽  
Author(s):  
Tereza Cristina Giannini ◽  
Daniel S. Chapman ◽  
Antonio Mauro Saraiva ◽  
Isabel Alves-dos-Santos ◽  
Jacobus C. Biesmeijer
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Biotic Interactions ◽  
Distribution Models

scholarly journals Scale dependency of joint species distribution models challenges interpretation of biotic interactions

2021 ◽  
Author(s):  
Christian König ◽  
Rafael O. Wüest ◽  
Catherine H. Graham ◽  
Dirk Nikolaus Karger ◽  
Thomas Sattler ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Biotic Interactions ◽  
Scale Dependency ◽  
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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