scholarly journals Multiple Glacial Refugia of the Low-Dispersal Ground Beetle Carabus irregularis: Molecular Data Support Predictions of Species Distribution Models

PLoS ONE ◽  
2013 ◽  
Vol 8 (4) ◽  
pp. e61185 ◽  
Author(s):  
Katharina Homburg ◽  
Claudia Drees ◽  
Martin M. Gossner ◽  
László Rakosy ◽  
Al Vrezec ◽  
...  
2020 ◽  
Vol 20 (4) ◽  
pp. 747-762
Author(s):  
Kamila S. Zając ◽  
Małgorzata Proćków ◽  
Krzysztof Zając ◽  
Daniel Stec ◽  
Dorota Lachowska-Cierlik

AbstractFaustina faustina is a conchologically highly diverse forest gastropod with several morphological forms. It is a Carpathian species, but it also occurs in northern isolated localities, where it was probably introduced. We performed the first phylogeographic analysis of 22 populations, based on three molecular markers: COI, ITS-2, and 28S rRNA. Genetic data were complemented by paleo-distribution models of spatial occupancy during the Last Glacial Maximum to strengthen inferences of refugial areas. We discovered high genetic variability of COI sequences with p-distances between haplotypes ranged from 0.2 to 18.1% (6.3–16.6% between clades). For nuclear markers, a haplotype distribution pattern was revealed. Species distribution models indicated a few potential refugia in the Carpathians, with the most climatically stable and largest areas in the Southern Carpathians. In some climate scenarios, putative microrefugia were also predicted in the Western and Eastern Carpathians, and in the Apuseni Mts. Our results suggest the glacial in situ survival of F. faustina and its Holocene expansion in the Sudetes. Although our genetic data as well as shell phenotypes showed considerable variation within and between studied populations, the molecular species delimitation approaches still imply only one single species. Our study contributes to the understanding of the impact of processes on shaping contemporary population genetic structure and diversity in low-dispersal, forest species.


2016 ◽  
Vol 43 (11) ◽  
pp. 2223-2236 ◽  
Author(s):  
Claudia Drees ◽  
Martin Husemann ◽  
Katharina Homburg ◽  
Patric Brandt ◽  
Petra Dieker ◽  
...  

2011 ◽  
Vol 56 (12) ◽  
pp. 2554-2566 ◽  
Author(s):  
KATHRIN THEISSINGER ◽  
MIKLÓS BÁLINT ◽  
PETER HAASE ◽  
JES JOHANNESEN ◽  
IRINA LAUBE ◽  
...  

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang

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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