122
Integration of Physical, Biological, and Socioeconomic Information
through, for example, hypertext links (e.g., Southern Appalachian Man and the Biosphere, 1996).
The latter is more flexible and adaptable, although
often harder and more expensive to develop and
control.
Particularly with spatial data, but also with temporal data, it is increasingly possible to obtain very
large volumes of data that complicate the data integration process. This may be unavoidable, given
a large spatial or temporal scope; nonetheless it is
worth being careful to match data to our needs. As
the costs of computer storage, memory, and processing power fall, this becomes less of an issue;
but data management remains a challenge.
At the core of working with information of different sorts and sources is managing and understanding the implications of different units and
scales. There are no simple answers here. We must
know our data and also understand issues of scale
in spatial data, sampling in space and time, and the
use of units. Standard texts in spatial and temporal
data analysis, GIS, and quantitative methods address these issues (Haines-Young et aI., 1993;
Fotheringham and Rogerson, 1994; Schneider,
1994). In addition, we should have a good understanding of the assumptions, methods, and purposes
under which the different kinds of data were collected. Information of this kind, termed metadata,
is necessary to highlight the possible ways that data
can be used--or should not be used.
Even more fundamental is the need to bridge the
perceived chasm between quantitative and qualitative information. Later sections will offer some specific comments on linking the two. At this stage,
the priority is to recognize the contributions of
both, and especially those of qualitative information, which are too often denigrated or ignored.
This means relating the different contributions of
both kinds of information to assessment and management goals, also remembering that assessment
and management take place in a context of human
perception and action (cf. Bums et al., 1985; Slocombe, 1995). Hence ignoring qualitative data from
human perceptions, recollections, and aspirations is
a recipe for eventual failure. An important dimension of this is comparing and relating the various
uncertainties in different kinds of qualitative data
(e.g., Cleaves, 1995).
9.3.1 Integration across Domains
and Disciplines
Beyond the prerequisites already noted, the most
critical step for integrating information across domains is to adopt a systemic view of the system under study (e.g., Open Systems Group, 1981; Weinberg, 1975). This includes developing a conceptual
model of the ecosystem, its subsystems, environment, flows, and hierarchies. Such a systems analysis provides a way to frame the assessment or planning problem, as well as a means of organizing
information in a hierarchy of relationships and
nested subsystems. It also requires assembling information from many domains for all but the most
trivial systems and quantitative and qualitative information for any but narrowly defined purposes.
A critical part of this step, and the next two as well,
is identifying key variables, processes, functions, and
boundaries. For examples of this, see Grzybowski
and Slocombe (1988) and Slocombe (1990). Boundaries are an especially necessary starting point (although they may also be a product, e.g., in representativeness assessment or ecological mapping).
It is necessary to establish, at least provisionally,
both physical limits to the area of interest (usually
based on multidisciplinary criteria) and disciplinary, informational limits to what is most relevant
(usually based on the goals of the assessment and
some initial understanding of the character of the
study area).
The next step is to use specialized, domainspecific knowledge to identify points of interaction
and connection between the system components
and, where possible, to quantify interactions and
develop a picture of dynamics and past, present,
and even future behaviors. This almost always depends on detailed knowledge and experience of the
system, at least some of it qualitative. Often it may
help to focus on particular concepts or perspectives
that foster identification of interrelationships in a
way suited to our goals, as discussed later under
conceptual tools.
Given the nature of the problems to be addressed
and the need to integrate across disciplines, it is
clear that multidisciplinary teams will often be
needed to facilitate the process. Such teams have
become reasonably common in the last 20 years in
resource and environmental management. However, the mere creation of a team does not guarantee results and integration, any more than does
commissioning a series of multidisciplinary research projects. Teams must be well chosen, focused, and skilled in integrative methods such as
systems ideas, be open to integration and disciplines other than their specialty, and have the time
and tools to do their job well (cf. Garcia, 1989; Giavelli, 1989). Some of these common tools are facilitated workshops, time for exploration and discussion, simulation modeling, and increasingly GIS
and decision support systems, discussed further in
Integration of Physical, Biological, and Socioeconomic Information
through, for example, hypertext links (e.g., Southern Appalachian Man and the Biosphere, 1996).
The latter is more flexible and adaptable, although
often harder and more expensive to develop and
control.
Particularly with spatial data, but also with temporal data, it is increasingly possible to obtain very
large volumes of data that complicate the data integration process. This may be unavoidable, given
a large spatial or temporal scope; nonetheless it is
worth being careful to match data to our needs. As
the costs of computer storage, memory, and processing power fall, this becomes less of an issue;
but data management remains a challenge.
At the core of working with information of different sorts and sources is managing and understanding the implications of different units and
scales. There are no simple answers here. We must
know our data and also understand issues of scale
in spatial data, sampling in space and time, and the
use of units. Standard texts in spatial and temporal
data analysis, GIS, and quantitative methods address these issues (Haines-Young et aI., 1993;
Fotheringham and Rogerson, 1994; Schneider,
1994). In addition, we should have a good understanding of the assumptions, methods, and purposes
under which the different kinds of data were collected. Information of this kind, termed metadata,
is necessary to highlight the possible ways that data
can be used--or should not be used.
Even more fundamental is the need to bridge the
perceived chasm between quantitative and qualitative information. Later sections will offer some specific comments on linking the two. At this stage,
the priority is to recognize the contributions of
both, and especially those of qualitative information, which are too often denigrated or ignored.
This means relating the different contributions of
both kinds of information to assessment and management goals, also remembering that assessment
and management take place in a context of human
perception and action (cf. Bums et al., 1985; Slocombe, 1995). Hence ignoring qualitative data from
human perceptions, recollections, and aspirations is
a recipe for eventual failure. An important dimension of this is comparing and relating the various
uncertainties in different kinds of qualitative data
(e.g., Cleaves, 1995).
9.3.1 Integration across Domains
and Disciplines
Beyond the prerequisites already noted, the most
critical step for integrating information across domains is to adopt a systemic view of the system under study (e.g., Open Systems Group, 1981; Weinberg, 1975). This includes developing a conceptual
model of the ecosystem, its subsystems, environment, flows, and hierarchies. Such a systems analysis provides a way to frame the assessment or planning problem, as well as a means of organizing
information in a hierarchy of relationships and
nested subsystems. It also requires assembling information from many domains for all but the most
trivial systems and quantitative and qualitative information for any but narrowly defined purposes.
A critical part of this step, and the next two as well,
is identifying key variables, processes, functions, and
boundaries. For examples of this, see Grzybowski
and Slocombe (1988) and Slocombe (1990). Boundaries are an especially necessary starting point (although they may also be a product, e.g., in representativeness assessment or ecological mapping).
It is necessary to establish, at least provisionally,
both physical limits to the area of interest (usually
based on multidisciplinary criteria) and disciplinary, informational limits to what is most relevant
(usually based on the goals of the assessment and
some initial understanding of the character of the
study area).
The next step is to use specialized, domainspecific knowledge to identify points of interaction
and connection between the system components
and, where possible, to quantify interactions and
develop a picture of dynamics and past, present,
and even future behaviors. This almost always depends on detailed knowledge and experience of the
system, at least some of it qualitative. Often it may
help to focus on particular concepts or perspectives
that foster identification of interrelationships in a
way suited to our goals, as discussed later under
conceptual tools.
Given the nature of the problems to be addressed
and the need to integrate across disciplines, it is
clear that multidisciplinary teams will often be
needed to facilitate the process. Such teams have
become reasonably common in the last 20 years in
resource and environmental management. However, the mere creation of a team does not guarantee results and integration, any more than does
commissioning a series of multidisciplinary research projects. Teams must be well chosen, focused, and skilled in integrative methods such as
systems ideas, be open to integration and disciplines other than their specialty, and have the time
and tools to do their job well (cf. Garcia, 1989; Giavelli, 1989). Some of these common tools are facilitated workshops, time for exploration and discussion, simulation modeling, and increasingly GIS
and decision support systems, discussed further in