Invasive alien species are non-indigenous taxa introduced to areas beyond their natural distribution and bio-geographical barriers by human activity, with important impacts on biodiversity, human health and ecosystem services. With the human population being higher than ever before and increasing, together with unprecedented rates of mobility of humans and goods, the introduction of new invasive species is more common than ever and is at the forefront of research in many disciplines such as ecology, epidemiology and food security.
Understanding the spread and successful introduction of invasive species is a highly complex as biological, social, geographic, economic and climatic factors influence the way an invasive species is introduced and determines the options available for its eventual detection and control.
With the rapid development of smart sensors, social networks, digital maps and remotely-sensed imagery; spatio-temporal data are more ubiquitous and richer than ever before. The availability of such large datasets (Big data) poses great challenges in data analysis. In addition, increased availability of computing power facilitates the use of computationally-intensive methods for the analysis of such data.
Thus new methods are needed to understand biological invasions. A Research Topic held in Environmental Informatics aims to address this topic.
Thematics could include:
· Machine learning methods (Neural networks, Random forests)
· Pattern analysis (Pattern formation; autocorrelation; clusters)
· Stochastic analysis (SPDE, SIR etc.)
· Bayesian modeling
· Big data analytics
· Spatiotemporal statistics
· Data visualization
· GIS; Trajectories and GPS tracking; Telemetry and Remote Sensing
· Agent based modelling calibrated with real data
· Decision making and risk assessment
· Network and connectivity analysis
· Co-occurrence and moving objects
This list is not exhaustive and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual. A key aim of the thematic is to maximise the use of the proposed methods/techniques by the scientific community and environmental stakeholders. To that end we strongly encourage contributors to upload their computer code, example applications or demonstrations of methods.
Please note that purely descriptive papers, or applications of widely used statistical methodologies such as species distribution modelling or maxent etc without any methodological improvement or detection of problems in the method are not within the scope of this thematic.
Invasive alien species are non-indigenous taxa introduced to areas beyond their natural distribution and bio-geographical barriers by human activity, with important impacts on biodiversity, human health and ecosystem services. With the human population being higher than ever before and increasing, together with unprecedented rates of mobility of humans and goods, the introduction of new invasive species is more common than ever and is at the forefront of research in many disciplines such as ecology, epidemiology and food security.
Understanding the spread and successful introduction of invasive species is a highly complex as biological, social, geographic, economic and climatic factors influence the way an invasive species is introduced and determines the options available for its eventual detection and control.
With the rapid development of smart sensors, social networks, digital maps and remotely-sensed imagery; spatio-temporal data are more ubiquitous and richer than ever before. The availability of such large datasets (Big data) poses great challenges in data analysis. In addition, increased availability of computing power facilitates the use of computationally-intensive methods for the analysis of such data.
Thus new methods are needed to understand biological invasions. A Research Topic held in Environmental Informatics aims to address this topic.
Thematics could include:
· Machine learning methods (Neural networks, Random forests)
· Pattern analysis (Pattern formation; autocorrelation; clusters)
· Stochastic analysis (SPDE, SIR etc.)
· Bayesian modeling
· Big data analytics
· Spatiotemporal statistics
· Data visualization
· GIS; Trajectories and GPS tracking; Telemetry and Remote Sensing
· Agent based modelling calibrated with real data
· Decision making and risk assessment
· Network and connectivity analysis
· Co-occurrence and moving objects
This list is not exhaustive and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual. A key aim of the thematic is to maximise the use of the proposed methods/techniques by the scientific community and environmental stakeholders. To that end we strongly encourage contributors to upload their computer code, example applications or demonstrations of methods.
Please note that purely descriptive papers, or applications of widely used statistical methodologies such as species distribution modelling or maxent etc without any methodological improvement or detection of problems in the method are not within the scope of this thematic.
