Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions

Mehmood, Kaleem; Anees, Shoaib Ahmad; Rehman, Akhtar; Rehman, Nazir Ur; Muhammad, Sultan; Shahzad, Fahad; Liu, Qijing; Alharbi, Sulaiman Ali; Alfarraj, Saleh; Ansari, Mohammad Javed; Khan, Waseem Razzaq

doi:10.1016/j.tfp.2024.100657

Trees, Forests and People

2024

DOI: 10.1016/j.tfp.2024.100657

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Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions

Kaleem Mehmood,

Shoaib Ahmad Anees,

Akhtar Rehman

et al.

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Cited by 4 publications

References 107 publications

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Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model

Li,

Liu,

et al. 2025

Environmental Research

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Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model

Li,

Liu,

et al. 2025

Environmental Research

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Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning

Anees,

Mehmood,

Rehman

et al. 2024

Environmental and Sustainability Indicators

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Investigating the Spatial Pattern of White Oak (Quercus alba L.) Mortality Using Ripley’s K Function Across the Ten States of the Eastern US

Khadka,

He,

Bardhan

2024

Forests

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White oak mortality is a significant concern in forest ecosystems due to its impact on biodiversity and ecosystem functions. Understanding the factors influencing white oak mortality is crucial for effective forest management and conservation efforts. In this study, we aimed to investigate the spatial pattern of WOM rates across the eastern US and explore the underlying processes behind the observed spatial patterns. Multicycle forest inventory and analysis data were compiled to capture all white oak plots. WOM data were selected across plot systems that utilized declining basal areas between two periods. Ripley’s K function was used to study the spatial pattern of WOM rates. Results showed clustered patterns of WOM rates at local and broad scales that may indicate stand-level competition and regional variables affecting white oaks’ dynamics across southern and northern regions. Results also indicated random patterns at broad scales, suggesting variations in topographic and hydrological conditions across the south and northern regions. However, the central region indicated both clustered and random patterns at the local scale that might be associated with inter-species competition and the possibility of environmental heterogeneity, respectively. Furthermore, uniform patterns of WOM rate at a broad scale across all regions might suggest regions with spatially homogeneous environmental factors acting on the dynamics of white oaks. This research might be helpful in identifying impacted areas of white oaks at varying scales. Future research is needed to comprehensively assess biotic and abiotic factors at various spatial scales aimed at mitigating WOM.

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Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions

Cited by 4 publications

References 107 publications

Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model

Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model

Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning

Investigating the Spatial Pattern of White Oak (Quercus alba L.) Mortality Using Ripley’s K Function Across the Ten States of the Eastern US

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