scholarly journals Incorporating species distribution model into the red list assessment and conservation of macrofungi: A case study with Ophiocordyceps sinensis

2020 ◽  
Vol 28 (1) ◽  
pp. 99-106 ◽  
Author(s):  
Li Yi ◽  
Tang Zhiyao ◽  
Yan Yujing ◽  
Wang Ke ◽  
Cai Lei ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Red List ◽  
Distribution Model ◽  
Ophiocordyceps Sinensis

Formation of disjunct plant distributions in Northeast Asia: a case study of Betula davurica using a species distribution model

Plant Ecology ◽  
2018 ◽  
Vol 219 (9) ◽  
pp. 1105-1115 ◽  
Author(s):  
Takuto Shitara ◽  
Yukito Nakamura ◽  
Tetsuya Matsui ◽  
Ikutaro Tsuyama ◽  
Haruka Ohashi ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Northeast Asia ◽  
Distribution Model ◽  
Plant Distributions

scholarly journals Terrestrial or marine species distribution model: Why not both? A case study with seabirds

2021 ◽  
Author(s):  
Henry Häkkinen ◽  
Silviu O. Petrovan ◽  
William J. Sutherland ◽  
Nathalie Pettorelli
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Marine Species ◽  
Distribution Model

scholarly journals Fit-for-Purpose: Species Distribution Model Performance Depends on Evaluation Criteria – Dutch Hoverflies as a Case Study

PLoS ONE ◽  
2013 ◽  
Vol 8 (5) ◽  
pp. e63708 ◽  
Author(s):  
Jesús Aguirre-Gutiérrez ◽  
Luísa G. Carvalheiro ◽  
Chiara Polce ◽  
E. Emiel van Loon ◽  
Niels Raes ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Evaluation Criteria ◽  
Model Performance ◽  
Distribution Model ◽  
Fit For Purpose

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.

scholarly journals The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Model Performance ◽  
Distribution Model ◽  
Science Data ◽  
Quality Filtering ◽  
The Impact
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