Global concern about the restoration of vegetation ecosystems has recently increased. The vegetation cover of drylands is sparse with large temporal and spatial fluctuations. This further affects the diversity and vulnerability of drylands under climate change and human interventions. Potential natural vegetation (PNV) is especially useful for raising public awareness about land degradation, estimating land potential, and vegetation restoration programs. As a reference, PNV could provide a clear insight into the development of sustainable ecological restoration programs in the face of current climate change. Nowadays, the massive development of machine learning-based approaches has shown great abilities to extract featured information and identification of the inherent correlations and patterns among complex ecological datasets. Additionally, different modeling techniques such as species distribution models (MAXENT) or spatial statistical techniques (e.g. geographically weighted models) are also widely used in ecological studies as a predictive tool. These techniques can provide a better understanding of evolutionary trends, trait dynamics, and spatial-temporal distribution of PNV, and operational planning for future vegetation restoration programs.
The specific objective of this research topic is to highlight the practical implications of different techniques for ecologists in modelling vegetation diversity, planning, and designing ecological restoration programs for degraded ecosystems across the world.
Therefore manuscripts including original research articles, reviews, and methods are welcome for submissions including but not limited to:
- The simulation of PNV distribution
- Evolutionary trends, trait dynamics, and biogeographic patterns of exotic and native vegetation species in different ecosystems
- Ecological dynamics and mechanisms using digitized data
- Analyzing the impacts of climate change and human activities on PNV in drylands
- Novel spatial statistical or machine learning algorithms used to simulate PNV
Global concern about the restoration of vegetation ecosystems has recently increased. The vegetation cover of drylands is sparse with large temporal and spatial fluctuations. This further affects the diversity and vulnerability of drylands under climate change and human interventions. Potential natural vegetation (PNV) is especially useful for raising public awareness about land degradation, estimating land potential, and vegetation restoration programs. As a reference, PNV could provide a clear insight into the development of sustainable ecological restoration programs in the face of current climate change. Nowadays, the massive development of machine learning-based approaches has shown great abilities to extract featured information and identification of the inherent correlations and patterns among complex ecological datasets. Additionally, different modeling techniques such as species distribution models (MAXENT) or spatial statistical techniques (e.g. geographically weighted models) are also widely used in ecological studies as a predictive tool. These techniques can provide a better understanding of evolutionary trends, trait dynamics, and spatial-temporal distribution of PNV, and operational planning for future vegetation restoration programs.
The specific objective of this research topic is to highlight the practical implications of different techniques for ecologists in modelling vegetation diversity, planning, and designing ecological restoration programs for degraded ecosystems across the world.
Therefore manuscripts including original research articles, reviews, and methods are welcome for submissions including but not limited to:
- The simulation of PNV distribution
- Evolutionary trends, trait dynamics, and biogeographic patterns of exotic and native vegetation species in different ecosystems
- Ecological dynamics and mechanisms using digitized data
- Analyzing the impacts of climate change and human activities on PNV in drylands
- Novel spatial statistical or machine learning algorithms used to simulate PNV
