scholarly journals Spatial Genetic Patterns and Distribution Dynamics of the Rare Oak Quercus chungii: Implications for Biodiversity Conservation in Southeast China

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 821 ◽  
Author(s):  
Jiang ◽  
Xu ◽  
Deng
Keyword(s):  
Genetic Diversity ◽  
Biodiversity Conservation ◽  
Species Distribution ◽  
Distribution Model ◽  
Genetic Admixture ◽  
Future Climate Change ◽  
Distribution Dynamics ◽  
Southeast China ◽  
Genetic Patterns ◽  
Nanling Mountains

A rapidly changing climate and frequent human activity influences the distribution and community structure of forests. Increasing our knowledge about the genetic diversity and distribution patterns of trees is helpful for forest conservation and management. In this study, nSSRs (nuclear simple sequence repeats) were integrated with a species distribution model (SDM) to investigate the spatial genetic patterns and distribution dynamics of Quercus chungii F.P.Metcalf, a rare oak in the subtropics of southeast China. A total of 188 individuals from 11 populations distributed across the natural range of Q. chungii were genotyped using nine nSSRs. The STRUCTURE analysis indicated that genetic admixture was present in all populations, but the population genetic variation and genetic differentiation were related to their geographical distributions. The SDM result indicated that Q. chungii retreated to the Nanling Mountains and adjacent areas during the Last Glacial Maximum (LGM) period, which corresponds to higher genetic diversity for populations in this region. Landscape genetic analysis showed that the Nanling Mountains served as a corridor for organism dispersal at the glacial and interglacial periods within the Quaternary. Based on these results, we propose that establishing nature reserves to protect the ecological corridor across the Nanling Mountains is necessary for the conservation of regional species genetic diversity, as well as the ecosystem of evergreen broadleaved forests in southern China. The study combines species distribution models and genetic diversity to provide new insight into biodiversity conservation and forest management under future climate change.

scholarly journals Ancient events and climate adaptive capacity shaped distinct chloroplast genetic structure in the oak lineages

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Mengxiao Yan ◽  
Ruibin Liu ◽  
Ying Li ◽  
Andrew L. Hipp ◽  
Min Deng ◽  
...  
Keyword(s):  
Climate Change ◽  
Genetic Diversity ◽  
Genetic Structure ◽  
Environmental Changes ◽  
Spatial Genetic Structure ◽  
Future Climate ◽  
Future Climate Change ◽  
Wide Range ◽  
Genetic Patterns ◽  
Genetic Structures

Abstract Background Understanding the origin of genetic variation is the key to predict how species will respond to future climate change. The genus Quercus is a species-rich and ecologically diverse woody genus that dominates a wide range of forests and woodland communities of the Northern Hemisphere. Quercus thus offers a unique opportunity to investigate how adaptation to environmental changes has shaped the spatial genetic structure of closely related lineages. Furthermore, Quercus provides a deep insight into how tree species will respond to future climate change. This study investigated whether closely related Quercus lineages have similar spatial genetic structures and moreover, what roles have their geographic distribution, ecological tolerance, and historical environmental changes played in the similar or distinct genetic structures. Results Despite their close relationships, the three main oak lineages (Quercus sections Cyclobalanopsis, Ilex, and Quercus) have different spatial genetic patterns and occupy different climatic niches. The lowest level and most homogeneous pattern of genetic diversity was found in section Cyclobalanopsis, which is restricted to warm and humid climates. The highest genetic diversity and strongest geographic genetic structure were found in section Ilex, which is due to their long-term isolation and strong local adaptation. The widespread section Quercus is distributed across the most heterogeneous range of environments; however, it exhibited moderate haplotype diversity. This is likely due to regional extinction during Quaternary climatic fluctuation in Europe and North America. Conclusions Genetic variations of sections Ilex and Quercus were significantly predicted by geographic and climate variations, while those of section Cyclobalanopsis were poorly predictable by geographic or climatic diversity. Apart from the different historical environmental changes experienced by different sections, variation of their ecological or climatic tolerances and physiological traits induced varying responses to similar environment changes, resulting in distinct spatial genetic patterns.

scholarly journals Influence of environmental factors on the genetic variation of the aquatic macrophyte Ranunculus subrigidus on the Qinghai-Tibetan Plateau

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Zhigang Wu ◽  
Xinwei Xu ◽  
Juan Zhang ◽  
Gerhard Wiegleb ◽  
Hongwei Hou
Keyword(s):  
Genetic Diversity ◽  
Tibetan Plateau ◽  
Environmental Factors ◽  
Local Adaptation ◽  
Aquatic Macrophyte ◽  
Spatial Genetic Structure ◽  
Long Distance ◽  
Elevation Gradients ◽  
Mantel Tests ◽  
Genetic Patterns

Abstract Background Due to the environmental heterogeneity along elevation gradients, alpine ecosystems are ideal study objects for investigating how ecological variables shape the genetic patterns of natural species. The highest region in the world, the Qinghai-Tibetan Plateau, is a hotspot for the studies of evolutionary processes in plants. Many large rivers spring from the plateau, providing abundant habitats for aquatic and amphibious organisms. In the present study, we examined the genetic diversity of 13 Ranunculus subrigidus populations distributed throughout the plateau in order to elucidate the relative contribution of geographic distance and environmental dissimilarity to the spatial genetic pattern. Results A relatively low level of genetic diversity within populations was found. No spatial genetic structure was suggested by the analyses of molecular variance, Bayesian clustering analysis and Mantel tests. Partial Mantel tests and multiple matrix regression analysis showed a significant influence of the environment on the genetic divergence of the species. Both climatic and water quality variables contribute to the habitat heterogeneity of R. subrigidus populations. Conclusions Our results suggest that historical processes involving long-distance dispersal and local adaptation may account for the genetic patterns of R. subrigidus and current environmental factors play an important role in the genetic differentiation and local adaptation of aquatic plants in alpine landscapes.

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.

scholarly journals The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Model Performance ◽  
Distribution Model ◽  
Science Data ◽  
Quality Filtering ◽  
The Impact

scholarly journals Modelling Maritime Pine (Pinus pinaster Aiton) Spatial Distribution and Productivity in Portugal: Tools for Forest Management

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 368
Author(s):  
Cristina Alegria ◽  
Natália Roque ◽  
Teresa Albuquerque ◽  
Paulo Fernandez ◽  
Maria Margarida Ribeiro
Keyword(s):  
Spatial Distribution ◽  
Forest Management ◽  
Species Distribution ◽  
Pinus Pinaster ◽  
Landscape Planning ◽  
Environmental Data ◽  
Maritime Pine ◽  
Future Climate Change ◽  
Sequential Simulation ◽  
Model Species

Research Highlights: Modelling species’ distribution and productivity is key to support integrated landscape planning, species’ afforestation, and sustainable forest management. Background and Objectives: Maritime pine (Pinus pinaster Aiton) forests in Portugal were lately affected by wildfires and measures to overcome this situation are needed. The aims of this study were: (1) to model species’ spatial distribution and productivity using a machine learning (ML) regression approach to produce current species’ distribution and productivity maps; (2) to model the species’ spatial productivity using a stochastic sequential simulation approach to produce the species’ current productivity map; (3) to produce the species’ potential distribution map, by using a ML classification approach to define species’ ecological envelope thresholds; and (4) to identify present and future key factors for the species’ afforestation and management. Materials and Methods: Spatial land cover/land use data, inventory, and environmental data (climate, topography, and soil) were used in a coupled ML regression and stochastic sequential simulation approaches to model species’ current and potential distributions and productivity. Results: Maritime pine spatial distribution modelling by the ML approach provided 69% fitting efficiency, while species productivity modelling achieved only 43%. The species’ potential area covered 60% of the country’s area, where 78% of the species’ forest inventory plots (1995) were found. The change in the Maritime pine stands’ age structure observed in the last decades is causing the species’ recovery by natural regeneration to be at risk. Conclusions: The maps produced allow for best site identification for species afforestation, wood production regulation support, landscape planning considering species’ diversity, and fire hazard mitigation. These maps were obtained by modelling using environmental covariates, such as climate attributes, so their projection in future climate change scenarios can be performed.

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