UW Aquatic & Fishery Sciences Quantitative Seminar

Christian Torgersen

Research Landscape Ecologist, USGS FRESC Cascadia Field Station;
and Assistant Professor, University of Washington, College of Forest Resources

Integrated landscape monitoring: Lessons learned from the UK, Norway, USA, and Canada

Coauthors:

Tracy Kugler, Oregon State University, Department of Geosciences, Corvallis
Andrea Woodward, USGS Forest and Rangeland Ecosystem Science Center, Olympic Field Station, Port Angeles, WA
Susan Benjamin, USGS Western Geographic Science Center, Menlo Park, CA
Guy Gelfenbaum, USGS Western Coastal and Marine Geology, Menlo Park, CA
Alicia Torregrosa, USGS Western Geographic Science Center, Menlo Park, CA
Tracy Fuentes, US Geological Survey, Seattle

Abstract

Most environmental monitoring is conducted either using in situ
measurements at localized sample points or with remotely sensed data over
a region. Moreover, monitoring efforts typically focus on a particular
component of the environment and thus are not integrated across scales and
disciplines. Integrated landscape monitoring (ILM) is designed to
incorporate multiple scales and to make connections between components by
investigating relationships between landscape patterns and ecological
outcomes. In order to gain insights into the practice of conducting
integrated landscape monitoring, we examined four national monitoring
programs: the Countryside Survey (Great Britain), the 3Q Programme
(Norway), the Environmental Monitoring and Assessment Program (USA), and
the Ecological Monitoring and Assessment Network (Canada). We describe
the major features of each program and compare their approaches to four
aspects of interest for ILM: (1) the suite of ecosystem components
monitored, (2) the relationship between statistical validity and the use
of monitoring for decision-making, (3) scales at which monitoring is
conducted, and (4) how relationships between landscape patterns and
ecological responses are assessed. The key lessons learned are: (1)
monitoring should err on the side of comprehensiveness as much as
possible, (2) the desire for statistical certainty must be balanced
against the need to provide information that is most useful for
decision-making, (3) scales at which monitoring is conducted should be
commensurate with the landscape, (4) it is important to incorporate a
nested, multiscale approach, and (5) ILM should be built around questions
that seek relationships between landscape patterns and ecosystem
processes.