scholarly journals Correction to: Future range dynamics of the red alga Capreolia implexa in native and invaded regions: contrasting predictions from species distribution models versus physiological knowledge

2021 ◽  
Author(s):  
Philipp Laeseke ◽  
Brezo Martínez ◽  
Andrés Mansilla ◽  
Kai Bischof
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Red Alga ◽  
Distribution Models ◽  
Range Dynamics

scholarly journals “Reconstructions of the past distribution of Testudo graeca mitochondrial lineages in the Middle East and Transcaucasia support multiple refugia since the Last Glacial Maximum”: A response to Turkozan et al. (2021)

2021 ◽  
pp. 201-203
Author(s):  
Flora Ihlow
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Testudo Graeca ◽  
The Past ◽  
Environmental Similarity ◽  
Range Dynamics ◽  
Multiple Refugia ◽  
The Last Glacial Maximum ◽  
The Last Glacial

Species distribution models (SDMs) are frequently used to characterise current, past or future realised environmental niches. Two recent studies applied different approaches to infer range dynamics in eastern subspecies of the spur-thighed tortoise Testudo graeca Linnaeus, 1758. We discuss differences in the conclusions of the two papers and use multivariate environmental similarity surface (MESS) analyses to show that the results of the study by Turkozan et al. (2021), recently published in the Herpetological Journal, are compromised by extrapolation and therefore have to be interpreted with caution.

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.

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
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