scholarly journals Species distribution models predict range expansion better than chance but not better than a simple dispersal model

2013 ◽  
Vol 256 ◽  
pp. 1-5 ◽  
Author(s):  
Marta Rodríguez-Rey ◽  
Alberto Jiménez-Valverde ◽  
Pelayo Acevedo
Keyword(s):  
Species Distribution ◽  
Range Expansion ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Dispersal Model ◽  
Better Than

scholarly journals Transferability and scalability of species distribution models: a test with sedentary marine invertebrates

2017 ◽  
Vol 74 (5) ◽  
pp. 766-778 ◽  
Author(s):  
Aaron M. Eger ◽  
Janelle M.R. Curtis ◽  
Marie-Josée Fortin ◽  
Isabelle M. Côté ◽  
Frédéric Guichard
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Marine Invertebrates ◽  
Predictive Accuracy ◽  
Distribution Models ◽  
Initial Information ◽  
Reference Models ◽  
Different Levels ◽  
Better Than

We found the predictive accuracy of species distribution models (SDMs) for sedentary marine invertebrates to be dependent on the methodology of their application. We explored three applications of SDMs: first a model tested at a scale smaller than at which it was trained (downscaled), second a model tested at scale larger than its training scale (upscaled), and third a model tested at the same scale but outside the extent for which it was trained (transferred). The accuracies of these models were compared with the “reference” models that were trained and tested at the same scale and extent. We found that downscaled SDMs had higher predictive accuracy than reference SDMs. Transferred and upscaled models had lower predictive accuracy than their reference counterparts but still performed better than random, making them potentially acceptable alternatives where information is lacking for imminent decisions or in cost-restricted scenarios. Our results provide insights into the techniques available for researchers and managers developing SDMs at varying scales, with different species, and with different levels of initial information.

Species distribution models as a tool to predict range expansion after reintroduction: A case study on Eurasian beavers ( Castor fiber )

2017 ◽  
Vol 37 ◽  
pp. 12-20 ◽  
Author(s):  
Sonia Smeraldo ◽  
Mirko Di Febbraro ◽  
Duško Ćirović ◽  
Luciano Bosso ◽  
Igor Trbojević ◽  
...  
Keyword(s):  
Species Distribution ◽  
Range Expansion ◽  
Species Distribution Models ◽  
Castor Fiber ◽  
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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