Analyzing Human-Landscape Interactions: Tools That Integrate
Authored by Li An, Alex Zvoleff
Date Published: 2014
DOI: 10.1007/s00267-012-0009-1
Sponsors:
United States National Science Foundation (NSF)
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Abstract
Humans have transformed much of Earth's land surface, giving rise to
loss of biodiversity, climate change, and a host of other environmental
issues that are affecting human and biophysical systems in unexpected
ways. To confront these problems, environmental managers must consider
human and landscape systems in integrated ways. This means making use of
data obtained from a broad range of methods (e.g., sensors, surveys), while taking into account new findings from the social and biophysical
science literatures. New integrative methods (including data fusion, simulation modeling, and participatory approaches) have emerged in
recent years to address these challenges, and to allow analysts to
provide information that links qualitative and quantitative elements for
policymakers. This paper brings attention to these emergent tools while
providing an overview of the tools currently in use for analysis of
human-landscape interactions. Analysts are now faced with a staggering
array of approaches in the human-landscape literature-in an attempt to
bring increased clarity to the field, we identify the relative strengths
of each tool, and provide guidance to analysts on the areas to which
each tool is best applied. We discuss four broad categories of tools:
statistical methods (including survival analysis, multi-level modeling, and Bayesian approaches), GIS and spatial analysis methods, simulation
approaches (including cellular automata, agent-based modeling, and
participatory modeling), and mixed-method techniques (such as
alternative futures modeling and integrated assessment). For each tool, we offer an example from the literature of its application in
human-landscape research. Among these tools, participatory approaches
are gaining prominence for analysts to make the broadest possible array
of information available to researchers, environmental managers, and
policymakers. Further development of new approaches of data fusion and
integration across sites or disciplines pose an important challenge for
future work in integrating human and landscape components.
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