72
et aI., 1993; Stohlgren et al., 1997c, 1998). This, in
tum, greatly influences the amount and complexity of ecological data by expanding the spatial and
temporal scales of observations and the detail of
each set of observations.
The purpose of this chapter is to highlight four
major features of data acquisition in ecological assessments: (1) clearly articulated goals and objectives; (2) a commitment to preserving the integrity,
longevity, and accessibility of the data for future
unforeseen uses; (3) a detailed vision of how the
data will be gathered, stored, summarized, statistically analyzed, displayed, and archived; and (4) an
understanding of the quality and limitations of the
data. Several successful survey, monitoring, and research programs have grappled with data acquisition issues for many years. The examples throughout this chapter are not exhaustive, and they have
bias toward vegetation. Examples serve two major
functions: to avoid "re-creating the wheel" and to
avoid potential pitfalls by taking advantage of the
hard-fought experiences of others.
5.2 Clearly Articulated Goals
and Objectives
The first and most important step in data acquisition
is to clearly articulate study goals and objectives
(Oppenheimer et al., 1974; Krebs, 1989). Goals may
be lofty, for example, to monitor changes in biological diversity, to detect the effects of acid deposition
or global climate change, or to evaluate the processes
influencing the structure and species composition of
forests. The objectives are well focused. Goals are
like a travel plan: we gather existing resources and
theories, select a clear direction and trail, acknowledge the unknowns, and keep the goal in mind
throughout the trip (Stohlgren, 1994).
Jones (1986) describes the process as "scoping"
and "problem definition," by which general problems are reduced to specific ones, specific issues
are identified, and priorities are set for specific data
acquisition needs. Setting objectives requires a
complete evaluation of existing data for spatial and
temporal completeness, accuracy, and precision.
This is usually no small task, but it is the only way
to identify the types and levels of data that are
needed.
Narrow objectives may eliminate the collection
of extraneous data and preclude unrealistic expectations (MacDonald et al., 1991). As the objectives
become clearly defined, we should be able to visualize data products (i.e., tables and figures) and
Data Acquisition
assess the potential limits to which study results
can be extrapolated spatially and temporally (see
Berkowitz et al., 1989). Specific objectives also
help in the selection of appropriate spatial scales,
sampling designs, and field methodologies.
5.3 Commitment to Preserving
the Integrity, Longevity, and
Accessibility of the Data
Most ecologists would agree that a far greater commitment to data management is needed. This commitment begins with a long-term view of the value
of well-collected data (Magnuson et al., 1991;
Risser, 1991). It is a little frightening that only
about 1.7% of ecological studies last at least five
field seasons (Tilman, 1989). It is more frightening that the data from a smaller percentage of studies are probably archived, documented appropriately, and accessible for other uses. How can we
change this?
A good rule of thumb is that about 20% to 25%
of the budget for ecological assessments needs to
be devoted to data and information management.
Whether a centralized or distributed architecture is
selected, several tasks must be fulfilled, including
quality control and quality assurance for field data;
data collection, transfer, storage, and archiving;
data and metadata tracking; and statistical analyses
(Shampine, 1993). One strength of the Long-Term
Ecological Research program is its hiring of data
managers, use of consistent hardware and software,
early development of network-wide data standards,
and attention to data synthesis (Stafford et aI.,
1986a, 1986b; Stafford, 1993).
Several other examples come to mind. For over
20 years, a program to monitor changes in forest
structure and demography over time by establishing large (1 hectare or greater) reference stands in
representative forest types has been underway
(Hawk et al., 1978). Because each tree is tagged
and mapped, large data sets amass quickly, with
yearly or periodic observations on seedling establishment, tree growth, mortality, and pathogen effects (Franklin and DeBell, 1988; Riegel et aI.,
1988; Parsons et aI., 1992). The program relies on
full-time data managers and a very strong commitment to standardized information management,
including data preservation.
The Smithsonian InstitutionlMan and the Biosphere Biological Diversity Program (SIIMAB) is
establishing permanent vegetation plots in repre-
et aI., 1993; Stohlgren et al., 1997c, 1998). This, in
tum, greatly influences the amount and complexity of ecological data by expanding the spatial and
temporal scales of observations and the detail of
each set of observations.
The purpose of this chapter is to highlight four
major features of data acquisition in ecological assessments: (1) clearly articulated goals and objectives; (2) a commitment to preserving the integrity,
longevity, and accessibility of the data for future
unforeseen uses; (3) a detailed vision of how the
data will be gathered, stored, summarized, statistically analyzed, displayed, and archived; and (4) an
understanding of the quality and limitations of the
data. Several successful survey, monitoring, and research programs have grappled with data acquisition issues for many years. The examples throughout this chapter are not exhaustive, and they have
bias toward vegetation. Examples serve two major
functions: to avoid "re-creating the wheel" and to
avoid potential pitfalls by taking advantage of the
hard-fought experiences of others.
5.2 Clearly Articulated Goals
and Objectives
The first and most important step in data acquisition
is to clearly articulate study goals and objectives
(Oppenheimer et al., 1974; Krebs, 1989). Goals may
be lofty, for example, to monitor changes in biological diversity, to detect the effects of acid deposition
or global climate change, or to evaluate the processes
influencing the structure and species composition of
forests. The objectives are well focused. Goals are
like a travel plan: we gather existing resources and
theories, select a clear direction and trail, acknowledge the unknowns, and keep the goal in mind
throughout the trip (Stohlgren, 1994).
Jones (1986) describes the process as "scoping"
and "problem definition," by which general problems are reduced to specific ones, specific issues
are identified, and priorities are set for specific data
acquisition needs. Setting objectives requires a
complete evaluation of existing data for spatial and
temporal completeness, accuracy, and precision.
This is usually no small task, but it is the only way
to identify the types and levels of data that are
needed.
Narrow objectives may eliminate the collection
of extraneous data and preclude unrealistic expectations (MacDonald et al., 1991). As the objectives
become clearly defined, we should be able to visualize data products (i.e., tables and figures) and
Data Acquisition
assess the potential limits to which study results
can be extrapolated spatially and temporally (see
Berkowitz et al., 1989). Specific objectives also
help in the selection of appropriate spatial scales,
sampling designs, and field methodologies.
5.3 Commitment to Preserving
the Integrity, Longevity, and
Accessibility of the Data
Most ecologists would agree that a far greater commitment to data management is needed. This commitment begins with a long-term view of the value
of well-collected data (Magnuson et al., 1991;
Risser, 1991). It is a little frightening that only
about 1.7% of ecological studies last at least five
field seasons (Tilman, 1989). It is more frightening that the data from a smaller percentage of studies are probably archived, documented appropriately, and accessible for other uses. How can we
change this?
A good rule of thumb is that about 20% to 25%
of the budget for ecological assessments needs to
be devoted to data and information management.
Whether a centralized or distributed architecture is
selected, several tasks must be fulfilled, including
quality control and quality assurance for field data;
data collection, transfer, storage, and archiving;
data and metadata tracking; and statistical analyses
(Shampine, 1993). One strength of the Long-Term
Ecological Research program is its hiring of data
managers, use of consistent hardware and software,
early development of network-wide data standards,
and attention to data synthesis (Stafford et aI.,
1986a, 1986b; Stafford, 1993).
Several other examples come to mind. For over
20 years, a program to monitor changes in forest
structure and demography over time by establishing large (1 hectare or greater) reference stands in
representative forest types has been underway
(Hawk et al., 1978). Because each tree is tagged
and mapped, large data sets amass quickly, with
yearly or periodic observations on seedling establishment, tree growth, mortality, and pathogen effects (Franklin and DeBell, 1988; Riegel et aI.,
1988; Parsons et aI., 1992). The program relies on
full-time data managers and a very strong commitment to standardized information management,
including data preservation.
The Smithsonian InstitutionlMan and the Biosphere Biological Diversity Program (SIIMAB) is
establishing permanent vegetation plots in repre-