Prediction Mapping Through Maxent Modeling Paves the Way for the Conservation of Rhododendron arboreum in Uttarakhand Himalayas

Bhandari, Maneesh S.; Meena, Rajendra K.; Shankhwar, Rajeev; Shekhar, Chander; Saxena, Jalaj; Kant, Rama; Pandey, Vijay Vardhan; Barthwal, Santan; Pandey, Shailesh; Chandra, Girish; Ginwal, H. S.

doi:10.1007/s12524-019-01089-0

Cited by 22 publications

(7 citation statements)

References 23 publications

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“…Current research has attempted to predict the impact of future climate change on the distribution of shrub species (Bhandari et al, 2020 ; Xu et al, 2019 ). Shrubs play an important role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood, and non‐wood products (Hageer et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Current and future distribution of the deciduous shrub Hydrangea macrophylla in China estimated by MaxEnt

Yan

Wang

Duan

et al. 2021

Ecology and Evolution

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Climate change has a significant impact on the growth and distribution of vegetation worldwide. Hydrangea macrophylla is widely distributed and considered a model species for studying the distribution and responses of shrub plants under climate change. These results can inform decision‐making regarding shrub plant protection, management, and introduction of germplasm resources, and are of great importance for formulating ecological countermeasures to climate change in the future. We used the maximum entropy model to predict the change, scope expansion/reduction, centroid movement, and dominant climate factors that restrict the growth and distribution of H. macrophylla in China under current and future climate change scenarios. It was found that both precipitation and temperature affect the distribution of suitable habitat for H. macrophylla. Akaike information criterion (AICc) was used to select the feature combination (FC) and the regularization multiplier (RM). After the establishment of the optimal model (FC = QP, RM = 0.5), the complexity and over‐fitting degree of the model were low (delta AICc = 0, omission rate = 0.026, difference between training and testing area under the curve values = 0.0009), indicating that it had high accuracy in predicting the potential geographical distribution of H. macrophylla (area under the curve = 0.979). Overall, from the current period to future, the potential suitable habitat of this species in China expanded to the north. The greenhouse effect caused by an increase in CO2 emissions would not only increase the area of high‐suitability habitat in Central China, but also expand the area of total suitable habitat in the north. Under the maximum greenhouse gas emission scenario (RCP8.5), the migration distance of the centroid was the longest (e.g., By 2070s, the centroids of total and highly suitable areas have shifted 186.15 km and 89.84 km, respectively).

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Section: Introductionmentioning

confidence: 99%

Current and future distribution of the deciduous shrub Hydrangea macrophylla in China estimated by MaxEnt

Yan

Wang

Duan

et al. 2021

Ecology and Evolution

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“…ex D. Don (Pinales: Pinaceae) in South Asia (Afghanistan, Pakistan, and India). Bhandari et al [41] highlighted BIO4, BIO7, BIO12, and BIO15 as major climatic factors influencing the distribution of the tree rhododendron Rhododendron arboreum Sm. (Ericales: Ericaceae) in the central Himalayas.…”

Section: Effects Of Environmental Variables On the Distribution Of S ...mentioning

confidence: 99%

Potential Distribution Projections for Senegalia senegal (L.) Britton under Climate Change Scenarios

Fang,

Shi,

Zhang

et al. 2024

Forests

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The gum acacia Senegalia senegal (L.) Britton (Fabales: Fabaceae) is a drought-tolerant plant belonging to the genus Acacia of the Leguminosae family, possessing significant economic and ecological value. Despite its importance, there is a knowledge gap regarding the potential impact of climate change on the distribution of S. senegal, crucial for the conservation of plant resources and optimizing its use in introductory silviculture. In this study, we selected 23 environmental variables and utilized the optimized maximum entropy (MaxEnt) model to analyze the key environmental factors affecting the distribution of S. senegal worldwide and simulate the current and future distribution range of S. senegal in Pakistan under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 climate change scenarios. The results highlight key environmental factors influencing the distribution of S. senegal, including BIO3 (isothermally), BIO4 (coefficient of seasonal variation of temperature), BIO11 (mean temperature of the coldest season), and BIO12 (annual precipitation). Regions with higher and less fluctuating temperatures exhibit a higher potential for S. senegal distribution. Currently, suitable habitats of S. senegal are concentrated in the southern region of Pakistan, covering provinces such as Punjab, Sindh, and Balochistan, with highly suitable habitats accounting for 6.06% of the total area. Under the current climatic conditions, this study identifies the spatial patterns of suitable habitats and their concentration in specific regions. With climate change, a notable expansion of suitable habitats towards higher latitudes is observed, with the most significant expansion under the extremely severe climate change scenario (SSP5-8.5), reaching 223.45% of the current level. The results of this study enhance our understanding of the dynamics of S. senegal distribution under climate change and offer valuable insights into the long-term introduction of S. senegal for afforestation and soil conservation in Pakistan. This study provides theoretical support for the sustainable development of the local ecosystem and socio-economy, emphasizing the importance of proactive measures to adapt to changing climatic conditions.

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“…MaxEnt (v 3.4.1) was utilized to determine habitat suitability and potential geographical distribution of field H. littoralis under the LGM, present, and future climate scenarios by combining precise climate variables with presence-only data [31]. We selected a cutoff to calculate the importance of the variable: 75% of the location data for model training, and the remaining 25% was used to validate the model, 1000 maximum iterations, and 10 replicates under subsample run types while maintaining other characteristics as defaults [31].…”

Section: Maxent Modeling and Parameter Settingmentioning

confidence: 99%

“…Subsequently, it has been widely adopted and is popular in several fields, including information classification, language processing, and species geographical distribution [25][26][27][28]. Based on the maximum entropy theory, the MaxEnt software is able to estimate the distribution (geographic range) of a species by determining the distribution (locations where the species has been found) carrying the maximum entropy subject to constraints derived from environmental conditions at recorded occurrence locations [29][30][31]. Compared to other models, MaxEnt has been widely adopted due to its optimal performance with small sample sizes relative to other modeling methods [32,33].…”

Section: Introductionmentioning

confidence: 99%

Potential Distribution Areas Prediction of Endangered Species - <i>Heritiera littoralis</i> Based on MaxEnt Modeling in China

Wang,

Liu,

Cheng

et al. 2024

Pol. J. Environ. Stud.

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The endangered semi-mangrove plant Heritiera littoralis occupies an overall area of less than 30 hm 2 in China. In this study, we employed the MaxEnt model to predict the distribution patterns of H. littoralis during the Last Glaciation Maximum, the present day, and the future. Drawing from 41 distributed records and 12 environmental factors within China, we investigated the species' potential habitats. To anticipate future scenarios, we assessed its distribution under the RCP4.5 greenhouse gas emission scenario for 2050 and 2070. As a result, four primary distribution areas emerged in Guangxi, Guangdong, Hainan, and Taiwan, signifying the main potential habitats for H. littoralis. Our findings indicate that the distribution centre of H. littoralis remains in Guangxi across the four time periods, with a projected eastward migration. By 2050, the species' habitat range is anticipated to contract, whereas, by 2070, the suitable habitat area is expected to expand. Moreover, we identify pivotal environmental factors influencing the potential distribution of H. littoralis, including precipitation of the warmest quarter (Bio 18), temperature seasonality (Bio 4), mean diurnal range (Bio 2), precipitation of the wettest month (Bio 13), and annual mean temperature (Bio 1). The outcomes of this study offer valuable data for comprehending the distribution of H. littoralis in China. Furthermore, they aid in pinpointing areas suitable for conservation management, both presently and in the future.

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Prediction Mapping Through Maxent Modeling Paves the Way for the Conservation of Rhododendron arboreum in Uttarakhand Himalayas

Cited by 22 publications

References 23 publications

Current and future distribution of the deciduous shrub Hydrangea macrophylla in China estimated by MaxEnt

Current and future distribution of the deciduous shrub Hydrangea macrophylla in China estimated by MaxEnt

Potential Distribution Projections for Senegalia senegal (L.) Britton under Climate Change Scenarios

Potential Distribution Areas Prediction of Endangered Species - <i>Heritiera littoralis</i> Based on MaxEnt Modeling in China

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