scholarly journals From topography to hydrology—The modifiable area unit problem impacts freshwater species distribution models

2020 ◽  
Vol 10 (6) ◽  
pp. 2956-2968
Author(s):  
Martin Friedrichs‐Manthey ◽  
Simone D. Langhans ◽  
Thomas Hein ◽  
Florian Borgwardt ◽  
Harald Kling ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Freshwater Species ◽  
Distribution Models ◽  
Area Unit ◽  
Modifiable Area Unit Problem

Long-term monitoring data meet freshwater species distribution models: Lessons from an LTER-site

2016 ◽  
Vol 65 ◽  
pp. 122-132 ◽  
Author(s):  
Mathias Kuemmerlen ◽  
Stefan Stoll ◽  
Andrea Sundermann ◽  
Peter Haase
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Monitoring Data ◽  
Freshwater Species ◽  
Distribution Models ◽  
Long Term Monitoring ◽  
Term Monitoring

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.

Can species distribution models and molecular tools help unravel disjunct distribution of Rhododendron arboreum?

2021 ◽  
Vol 100 (1) ◽  
Author(s):  
Avik Ray ◽  
Rajasri Ray ◽  
Rani M. Saggere ◽  
G. Ravikanth
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Disjunct Distribution ◽  
Molecular Tools ◽  
Distribution Models ◽  
Rhododendron Arboreum

scholarly journals The effects of climate change on Australia’s only endemic Pokémon: Measuring bias in species distribution models

2021 ◽  
Author(s):  
Dan L. Warren ◽  
Alex Dornburg ◽  
Katerina Zapfe ◽  
Teresa L. Iglesias
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Measuring Bias

The relationship between scale and predictor variables in species distribution models applied to conservation

2021 ◽  
Author(s):  
M. R. Oliveira ◽  
W. M. Tomas ◽  
N. M. R. Guedes ◽  
A.T. Peterson ◽  
J. K. Szabo ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Predictor Variables ◽  
Distribution Models ◽  
The Relationship

scholarly journals Species distribution models for invasive Eurasian watermilfoil highlight the importance of data quality and limitations of discrimination accuracy metrics

2021 ◽  
Author(s):  
Shyam M. Thomas ◽  
Michael R. Verhoeven ◽  
Jake R. Walsh ◽  
Daniel J. Larkin ◽  
Gretchen J. A. Hansen
Keyword(s):  
Data Quality ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Discrimination Accuracy ◽  
Distribution Models ◽  
Eurasian Watermilfoil
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