Different GCMs yet similar outcome: predicting the habitat distribution of Shorea robusta C.F. Gaertn. in the Indian Himalayas using CMIP5 and CMIP6 climate models

Kaur, Sharanjeet; Kaushal, Siddhartha; Adhikari, Dibyendu; Raj, Krishna; Rao, K. S.; Tandon, Rajesh; Goel, Shailendra; Barik, Saroj Kanta; Baishya, Ratul

doi:10.1007/s10661-023-11317-3

Cited by 2 publications

(1 citation statement)

References 81 publications

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“…RCP4.5 is considered a moderate emission scenario, assuming effective global climate change mitigation measures to limit the rise in global average temperature. In contrast, RCP8.5 is a high-emission scenario, assuming continuous growth in greenhouse gas emissions, leading to more significant global warming and environmental impacts [39,40]. Based on the aforementioned data sources, in this study, we obtained and calculated the following factors: (1) annual mean temperature (TEM, K); (2) maximum temperature of the warmest month each year (TEM max , K); (3) minimum temperature of the coldest month each year (TEM min , K); (4) temperature seasonality (TS, K), measured using the standard deviation of temperature, with the formula as follows:…”

Section: Current and Future Environmental Datamentioning

confidence: 99%

Predicting the Global Potential Suitable Distribution of Fall Armyworm and Its Host Plants Based on Machine Learning Models

Huang,

Dong,

Huang

et al. 2024

Remote Sensing

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The fall armyworm (Spodoptera frugiperda) (J. E. Smith) is a widespread, polyphagous, and highly destructive agricultural pest. Global climate change may facilitate its spread to new suitable areas, thereby increasing threats to host plants. Consequently, predicting the potential suitable distribution for the fall armyworm and its host plants under current and future climate scenarios is crucial for assessing its outbreak risks and formulating control strategies. This study, based on remote sensing assimilation data and plant protection survey data, utilized machine learning methods (RF, CatBoost, XGBoost, LightGBM) to construct potential distribution prediction models for the fall armyworm and its 120 host plants. Hyperparameter methods and stacking ensemble method (SEL) were introduced to optimize the models. The results showed that SEL demonstrated optimal performance in predicting the suitable distribution for the fall armyworm, with an AUC of 0.971 ± 0.012 and a TSS of 0.824 ± 0.047. Additionally, LightGBM and SEL showed optimal performance in predicting the suitable distribution for 47 and 30 host plants, respectively. Overlay analysis suggests that the overlap areas and interaction links between the suitable areas for the fall armyworm and its host plants will generally increase in the future, with the most significant rise under the RCP8.5 climate scenario, indicating that the threat to host plants will further intensify due to climate change. The findings of this study provide data support for planning and implementing global and intercontinental long-term pest management measures aimed at mitigating the impact of the fall armyworm on global food production.

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Section: Current and Future Environmental Datamentioning

confidence: 99%