Prediction of Potential Suitable Distribution Areas of Quasipaa spinosa in China Based on MaxEnt Optimization Model

Hou, Jinliang; Ji, Xiang; Li, Deliang; Liu, Xinhua

doi:10.3390/biology12030366

Cited by 14 publications

(10 citation statements)

References 40 publications

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“…The accuracy of the model prediction results was evaluated using the receiver operation characteristic (ROC) area under the curve (AUC) and true skill statistic (TSS) under current climate conditions. The closer the AUC and TSS values were close to 1, indicating a high prediction accuracy ( Hou et al., 2023 ). The model prediction performance was deemed when the AUC and TSS values were greater than 0.9, and 0.75, respectively ( Allouche et al., 2006 ; Franco et al., 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Potentially suitable habitats of Daodi goji berry in China under climate change

Li,

Deng,

Duan

et al. 2024

Front. Plant Sci.

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IntroductionGoji berry (Lycium barbarum L.) is a famous edible and medicinal herb worldwide with considerable consumption. The recent cultivation of goji berries in the Daodi region was seriously reduced due to increased production costs and the influence of policy on preventing nongrain use of arable land in China. Consequently, production of Daodi goji berry was insufficient to meet market demands for high-quality medicinal materials. Searching for regions similar to the Daodi region was necessary.MethodsThe MaxEnt model was used to predicted the current and future potential regions suitable for goji berry in China based on the environmental characteristics of the Daodi region (including Zhongning County of Zhongwei prefecture-level city, and its surroundings), and the ArcGIS software was used to analyze the changes in its suitable region.ResultsThe results showed that when the parameters were FC = LQHP and RM = 2.1, the MaxEnt model was optimal, and the AUC and TSS values were greater than 0.90. The mean temperature and precipitation of the coldest quarter were the most critical variables shaping the distribution of Daodi goji berries. Under current climate conditions, the suitable habitats of the Daodi goji berry were 45,973.88 km2, accounting for 0.48% of China’s land area, which were concentrated in the central and western Ningxia Province (22,589.42 km2), and the central region of Gansu Province (18,787.07 km2) bordering western Ningxia. Under future climate scenarios, the suitable area was higher than that under current climate conditions and reached the maximum under RCP 6.0 (91,256.42 km2) in the 2050s and RCP 8.5 (82,459.17 km2) in the 2070s. The expansion regions were mainly distributed in the northeast of the current suitable ranges, and the distributional centroids were mainly shifted to the northeast. The moderately and highly suitable overlapping habitats were mainly distributed in Baiyin (7,241.75 km2), Zhongwei (6,757.81 km2), and Wuzhong (5, 236.87 km2) prefecture-level cities.DiscussionIn this stduy, MaxEnt and ArcGIS were applied to predict and analyze the suitable habitats of Daodi goji berry in China under climate change. Our results indicate that climate warming is conducive to cultivating Daodi goji berry and will not cause a shift in the Daodi region. The goji berry produced in Baiyin could be used to satisfy the demand for high-quality medicinal materials. This study addresses the insufficient supply and guides the cultivation of Daodi goji berry.

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Section: Methodsmentioning

confidence: 99%

Potentially suitable habitats of Daodi goji berry in China under climate change

Li,

Deng,

Duan

et al. 2024

Front. Plant Sci.

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“…The average value of the MaxEnt model output results was imported into ArcGIS, and the "asc" format file was converted into raster data using the conversion tool [63]. The natural breaks (Jenks) method was used to reclassify the results of the simulated T. nanus' suitable area, which was divided into four suitable levels: an unsuitable area (0-0.2), a less suitable area (0.2-0.4), a medially suitable area (0.4-0.6), and a highly suitable area (0.6-1) [64,65]. The projection coordinate system chosen in this study was the WGS 1984 UTM Zone 48N and the proportion and area of each level of the suitability area were calculated.…”

Section: Classification Of Potentially Suitable Areamentioning

confidence: 99%

Response of Extremely Small Populations to Climate Change—A Case of Trachycarpus nanus in Yunnan, China

Wang,

et al. 2024

Biology

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Climate change affects the geographical distribution of plant species. Rare Trachycarpus nanus with a narrow distribution range, high medicinal value and extremely small population is facing increasing extinction risks under global climate change. In this study, 96 recorded occurrences and 23 environmental factors are used to predict the potential suitable area of T. nanus based on the optimized MaxEnt (3.4.4) model and ArcGIS (10.7) software. The results show that when the parameters are FC = LQ and RM = 1, the MaxEnt model is optimal and AUC = 0.946. The distribution patterns were predicted in the past, present, and four future phases, i.e., 2021–2040 (2030), 2041–2060 (2050), 2061–2080 (2070), and 2081–2100 (2090). The main factors are the annual precipitation (bio12), mean temperature of the coldest quarter (bio11), temperature seasonality (bio4), precipitation of the wettest quarter (bio16), and isothermality (bio3). The potential distribution of T. nanus is primarily concentrated in central Chuxiong, encompassing a total potential suitable area of 5.65 × 104 km2. In historical periods, the total habitat area is smaller than that in the present. In the future, the potential suitable area is generally increased. The centroid analysis shows that T. nanus will move to a high-altitude area and to the southeast. But its dispersal capacity may not keep up with the climate change rate. Therefore, additional protection sites for this species should be appropriately established and the habitat connectivity should be enhanced.

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“…Beyond predicting species distribution, these models have become an important decision-making tool for a variety of ecogeographical applications, such as identifying potential conservation areas, determining potential locations for the distribution of sensitive and invasive species, and mapping the spread of forest disease. These types of modeling, by combining separate environmental factors with spatial data of species distribution, with the help of statistical models such as Generalized Linear Model (GLM) and Gradient Boosting Model (GBM) among others, and by using machine learning methods, seek to find regular and logical relationships between species distribution and different variables 10 – 12 .…”

Section: Introductionmentioning

confidence: 99%

Potential distribution of Biscogniauxia mediterranea and Obolarina persica causal agents of oak charcoal disease in Iran’s Zagros forests

BakhshiGanje,

Mahmoodi,

Ahmadi

et al. 2024

Sci Rep

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In Iran, native oak species are under threat from episodes of Charcoal Disease, a decline syndrome driven by abiotic stressors (e.g. drought, elevated temperature) and biotic components, Biscogniauxia mediterranea (De Not.) Kuntze and Obolarina persica (M. Mirabolfathy). The outbreak is still ongoing and the country’s largest ever recorded. Still, the factors driving its’ epidemiology in time and space are poorly known and such knowledge is urgently needed to develop strategies to counteract the adverse effects. In this study, we developed a generic framework based on experimental, machine-learning algorithms and spatial analyses for landscape-level prediction of oak charcoal disease outbreaks. Extensive field surveys were conducted during 2013–2015 in eight provinces (more than 50 unique counties) in the Zagros ecoregion. Pathogenic fungi were isolated and characterized through morphological and molecular approaches, and their pathogenicity was assessed under controlled water stress regimes in the greenhouse. Further, we evaluated a set of 29 bioclimatic, environmental, and host layers in modeling for disease incidence data using four well-known machine learning algorithms including the Generalized Linear Model, Gradient Boosting Model, Random Forest model (RF), and Multivariate Adaptive Regression Splines implemented in MaxEnt software. Model validation statistics [Area Under the Curve (AUC), True Skill Statistics (TSS)], and Kappa index were used to evaluate the accuracy of each model. Models with a TSS above 0.65 were used to prepare an ensemble model. The results showed that among the different climate variables, precipitation and temperature (Bio18, Bio7, Bio8, and bio9) in the case of O. persica and similarly, gsl (growing season length TREELIM, highlighting the warming climate and the endophytic/pathogenic nature of the fungus) and precipitation in case of B. mediterranea are the most important influencing variables in disease modeling, while near-surface wind speed (sfcwind) is the least important variant. The RF algorithm generates the most robust predictions (ROC of 0.95; TSS of 0.77 and 0.79 for MP and OP, respectively). Theoretical analysis shows that the ensemble model (ROC of 0.95 and 0.96; TSS = 0.79 and 0.81 for MP and OP, respectively), can efficiently be used in the prediction of the charcoal disease spatiotemporal distribution. The oak mortality varied ranging from 2 to 14%. Wood-boring beetles association with diseased trees was determined at 20%. Results showed that water deficiency is a crucial component of the oak decline phenomenon in Iran. The Northern Zagros forests (Ilam, Lorestan, and Kermanshah provinces) along with the southern Zagros forests (Fars and Kohgilouyeh va-Boyer Ahmad provinces) among others are the most endangered areas of potential future pandemics of charcoal disease. Our findings will significantly improve our understanding of the current situation of the disease to pave the way against pathogenic agents in Iran.

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Prediction of Potential Suitable Distribution Areas of Quasipaa spinosa in China Based on MaxEnt Optimization Model

Cited by 14 publications

References 40 publications

Potentially suitable habitats of Daodi goji berry in China under climate change

Potentially suitable habitats of Daodi goji berry in China under climate change

Response of Extremely Small Populations to Climate Change—A Case of Trachycarpus nanus in Yunnan, China

Potential distribution of Biscogniauxia mediterranea and Obolarina persica causal agents of oak charcoal disease in Iran’s Zagros forests

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