Modelling Distributions of Asian and African Rice Based on MaxEnt

Lin, Yunan; Wang, Hao; Chen, Yanqing; Tan, Jiarui; Hong, Jingpeng; Yan, Shen; Cao, Yihua; Wei, Fang

doi:10.3390/su15032765

Cited by 4 publications

(3 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As many taxa are adapted to extreme climates, pests and diseases, and poor soils, they may provide valuable agronomic and nutritional traits for rice improvement (Dempewolf et al, 2014). Ideally, their genetic diversity should be conserved ex situ and in situ, as complementary conservation strategies (Khoury, Carver, Greene, et al, 2020; Lin et al, 2023). However, preliminary global assessments of the ex situ conservation of RWR have revealed significant gaps in preserving their genetic diversity (Castañeda‐Álvarez et al, 2016; Khoury, Carver, Greene, et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Global potential distributions and conservation status of rice wild relatives

Lin,

Chen,

Wang

et al. 2024

Plants People Planet

Self Cite

View full text Add to dashboard Cite

Societal Impact StatementRice wild relatives (RWR) provide valuable genetic resources for modern rice breeding, yet knowledge gaps constrain their conservation and further utilization. To address these gaps, the potential distributions of 22 RWR taxa were modeled, and their conservation statuses were assessed. Most taxa were identified as medium priority for further conservation. Further ex‐situ collecting hotspots are in Southeast and South Asia, Northern Australia, West Africa, and tropical Americas, while habitat protection lies in Southeast and South Asia, Northern Australia, and West Africa. Climate change may shift equatorial habitats to higher latitudes and mountain habitats to higher altitudes.Summary Rice wild relatives (RWR) provide valuable genetic resources for modern rice breeding. However, knowledge gaps on their geographic distributions and conservation status constrain their conservation and further utilization. To fill these critical gaps, we modeled the potential distributions of 22 RWR taxa under current climate scenarios, assessed their conservation status, both ex situ (in genebanks or botanical gardens) and in situ (in protected areas), and examined changes in taxa richness of RWR in predicted areas under future climate scenarios. The RWR were primarily distributed in tropical Asia to tropical Australia, tropical Africa, and South and Central America. We identified 4 out of 22 taxa as high priority (HP) for further conservation action and 18 taxa as medium priority (MP). Hotspots requiring further collecting for ex situ conservation are concentrated in Southeast and South Asia, Northern Australia, West Africa, and tropical Americas. Meanwhile, habitat protection should be enhanced in Southeast and South Asia, Northern Australia, and West Africa. Under future climate change, suitable habitats near the equator are expected to shift toward higher latitudes and some in lower‐latitude basins may become unsuitable due to excessive heat, resulting in decreased taxa richness in these areas. Additionally, suitable habitats in high mountain areas may shift to higher altitudes, potentially augmenting taxa richness in the highlands. Our findings provide vital insights to guide future rescue conservation efforts for RWR.

show abstract

Section: Introductionmentioning

confidence: 99%

Global potential distributions and conservation status of rice wild relatives

Lin,

Chen,

Wang

et al. 2024

Plants People Planet

Self Cite

View full text Add to dashboard Cite

show abstract

“…It ensures unbiased prediction results by making objective inferences about the allocation of unidentified species derived from a restricted set of recognized samples and the maximum entropy principle [14]. The MaxEnt model is frequently used to evaluate the suitability of distribution for plants, animals, and bacteria [15][16][17]. It is commonly to predict the suitable habitat for endangered and protected plants.…”

Section: Introductionmentioning

confidence: 99%

Potentially Suitable Area and Change Trends of <i>Tulipa iliensis</i> under Climate Change

Qin,

Liu,

Tian

et al. 2024

Phyton

View full text Add to dashboard Cite

Tulipa iliensis, as a wild plant resource, possesses high ornamental value and can provide abundant parental materials for tulip breeding. The objective of this research was to forecast the worldwide geographical spread of Tulipa iliensis by considering bioclimatic, soil, and topographic variables, the findings of this research can act as a benchmark for the conservation, management, and utilization of Tulipa iliensis as a wild plant resource. Research results indicate that all 12 models have an area under curve (AUC) of the receiver operating characteristic curve (ROC) values greater than 0.968 for the paleoclimatic, current, and future climate scenarios, this suggests an exceptionally high level of predictive accuracy for the models. The distribution of Tulipa iliensis is influenced by several key factors. These factors include the mean temperature of the driest quarter (Bio9), calcium carbonate content (T_CACO 3 ), slope, precipitation of the driest month (Bio14), Basic saturation (T_BS), and precipitation of the coldest quarter (Bio19). During the three paleoclimate climate scenarios, the appropriate habitats for Tulipa iliensis showed a pattern of expansion-contraction expansion. Furthermore, the total suitable area accounted for 13.38%, 12.28%, and 13.28% of the mainland area, respectively. According to the current climate scenario, the High-suitability area covers 61.78472 × 10 4 km 2 , which accounts for 6.57% of the total suitable area, The Midsuitability area covers 190.0938 × 10 4 km 2 , accounting for 20.2% of the total suitable area, this represents a decrease of 63.53%~67.13% compared to the suitable area of Tulipa iliensis under the paleoclimate scenario. Under the Shared Socioeconomic Pathways (SSP) scenarios, in 2050 and 2090, Tulipa iliensis is projected to experience a decrease in the High, Mid, and Low-suitability areas under the SSP126 climate scenario by 7.10% ~12.96%, 2.96%~4.27% and 4.80%~7.96%, respectively. According to the SSP245 scenario, the high suitability area experienced a slight expansion of 2.26% in 2050, but a reduction of 6.32% in 2090. In the SSP370 scenario, the High-suitability areas had a larger reduction rate of 11.24% in 2050, while the Mid-suitability and Low-suitability areas had smaller expansion rates of 0.36% and 4.86%, respectively. In 2090, the High-suitability area decreased by 4.84%, while the Mid and Low-suitability areas experienced significant expansions of 15.73% and 45.89%, respectively. According to the SSP585 scenario, in the future, the High, Mid, and Low-suitability areas are projected to increase by 5.09%~7.21%, 7.57%~17.66%, and 12.30%~48.98%, respectively. The research offers enhanced theoretical direction for preserving Tulipa iliensis' genetic variety amidst evolving climatic scenarios.

show abstract

“…Rice ( Oryza sativa L. ) is one of the most important food crops in the world, supporting as a staple food for more than half of the global population [ 1 , 2 ]. The increase in rice production will have a significant impact on world food security, making it necessary to explore new strategies to improve rice yield [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

NRTPredictor: identifying rice root cell state in single-cell RNA-seq via ensemble learning

Wang,

Lin,

Yan

et al. 2023

Plant Methods

Self Cite

View full text Add to dashboard Cite

Background Single-cell RNA sequencing (scRNA-seq) measurements of gene expression show great promise for studying the cellular heterogeneity of rice roots. How precisely annotating cell identity is a major unresolved problem in plant scRNA-seq analysis due to the inherent high dimensionality and sparsity. Results To address this challenge, we present NRTPredictor, an ensemble-learning system, to predict rice root cell stage and mine biomarkers through complete model interpretability. The performance of NRTPredictor was evaluated using a test dataset, with 98.01% accuracy and 95.45% recall. With the power of interpretability provided by NRTPredictor, our model recognizes 110 marker genes partially involved in phenylpropanoid biosynthesis. Expression patterns of rice root could be mapped by the above-mentioned candidate genes, showing the superiority of NRTPredictor. Integrated analysis of scRNA and bulk RNA-seq data revealed aberrant expression of Epidermis cell subpopulations in flooding, Pi, and salt stresses. Conclusion Taken together, our results demonstrate that NRTPredictor is a useful tool for automated prediction of rice root cell stage and provides a valuable resource for deciphering the rice root cellular heterogeneity and the molecular mechanisms of flooding, Pi, and salt stresses. Based on the proposed model, a free webserver has been established, which is available at https://www.cgris.net/nrtp.

show abstract

Modelling Distributions of Asian and African Rice Based on MaxEnt

Cited by 4 publications

References 48 publications

Global potential distributions and conservation status of rice wild relatives

Global potential distributions and conservation status of rice wild relatives

Potentially Suitable Area and Change Trends of <i>Tulipa iliensis</i> under Climate Change

NRTPredictor: identifying rice root cell state in single-cell RNA-seq via ensemble learning

Contact Info

Product

Resources

About