Phylogeography in continuous space: coupling species distribution models and circuit theory to assess the effect of contiguous migration at different climatic periods on genetic differentiation inBusseola fusca(Lepidoptera: Noctuidae)

2014 ◽  
Vol 23 (9) ◽  
pp. 2313-2325 ◽  
Author(s):  
S. Dupas ◽  
B. le Ru ◽  
A. Branca ◽  
N. Faure ◽  
G. Gigot ◽  
...  
Keyword(s):  
Genetic Differentiation ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Circuit Theory ◽  
Distribution Models ◽  
Continuous Space ◽  
Lepidoptera Noctuidae

scholarly journals Phylogeographical Structure of Liquidambar formosana Hance Revealed by Chloroplast Phylogeography and Species Distribution Models

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 858 ◽  
Author(s):  
Sun ◽  
Lin ◽  
Huang ◽  
Ye ◽  
Lai ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Genetic Differentiation ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Central China ◽  
Recent Common Ancestor ◽  
Interglacial Period ◽  
Last Interglacial ◽  
Distribution Models ◽  
Last Interglacial Period

To understand the origin and evolutionary history, and the geographical and historical causes for the formation of the current distribution pattern of Lquidambar formosana Hance, we investigated the phylogeography by using chloroplasts DNA (cpDNA) non-coding sequences and species distribution models (SDM). Four cpDNA intergenic spacer regions were amplified and sequenced for 251 individuals from 25 populations covering most of its geographical range in China. A total of 20 haplotypes were recovered. The species had a high level of chloroplast genetic variation (Ht = 0.909 ± 0.0192) and a significant phylogeographical structure (genetic differentiation takes into account distances among haplotypes (Nst) = 0.730 > population differentiation that does not consider distances among haplotypes (Gst) = 0.645; p < 0.05), whereas the genetic variation within populations (Hs = 0.323 ± 0.0553) was low. The variation of haplotype mainly occurred among populations (genetic differentiation coefficient (Fst) = 0.73012). The low genetic diversity within populations may be attributed to the restricted gene flow (Nm = 0.18). The time of the most recent common ancestor for clade V mostly distributed in Southwestern China, Central China, Qinling and Dabieshan mountains was 10.30 Ma (95% Highest posterior density (HPD): 9.74–15.28) dating back to the middle Miocene, which revealed the genetic structure of L. formosana was of ancient origin. These results indicated that dramatic changes since the Miocene may have driven the ancestors of L. formosana to retreat from the high latitudes of the Northern Hemisphere to subtropical China in which the establishment and initial intensification of the Asian monsoon provided conditions for their ecological requirements. This scenario was confirmed by the fossil record. SDM results indicated there were no contraction–expansion dynamics, and there was a stable range since the last interglacial period (LIG, 130 kya). Compared with the population expansion detected by Fu’s Fs value and the mismatch distribution, we speculated the expansion time may happen before the interglacial period. Evidence supporting L. formosana was the ancient origin and table range since the last interglacial period.

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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