PRESERVE SELECTION
MODELING IN THE COLUMBIA PLATEAU
INTRODUCTION
Statement of the Problem
The Nature Conservancy (TNC) identifies portfolios of sites and
strategies that will maintain viable native species and representative
plant communities. TNC wants to modify its approach such that it
can produce a conservation plan that will provide guidance for TNC
field offices and agency and non-governmental partners on how to
pursue protection of this portfolio of sites, and how to integrate
various conservation strategies (which might include actions such
as influencing particular policies, zoning, and identifying key
influences on critical ecological processes) with land acquisition
and/or management designation of particular sites. Further, TNC
needs recommendations for how they should implement this ecoregional
conservation planning approach throughout the organization, in contrast
to its current state or national scope of planning.
TNC contracted with the UCSB Biogeography Lab to assist in developing
a prototype planning process for the Columbia Plateau ecoregion.
The prototype was to integrate the spatial analysis functions of
a geographic information system (GIS) with an optimization model
for designing alternative portfolios. Three questions were germane
to the development of this prototype application:
- What set
of site selection rules provides the most efficient method for
designing and assembling a portfolio of sites to maintain all
viable native species and community types within a target ecoregion
(i.e., how can TNC maximize the amount of biodiversity protected
relative to the given number of conservation sites or amount of
land area)?
- How sensitive
is the portfolio to the way in which biodiversity is measured
(e.g., what are the effects of using a coarse-filter (alliances
from Gap Analysis) or fine-filter (rare element occurrences for
species and plant community associations from Natural Heritage
programs)?
- How can TNC
integrate programmatic, economic and socio-political factors into
the portfolio design process without sacrificing its biodiversity
goals?
Reserve system
planning over large regions can be somewhat artificially divided
into three stages: 1) setting of goals and priorities, 2) site selection,
and 3) reserve design. The first two stages are generally undertaken
using relatively coarse survey information, whereas the final stage
requires very detailed analyses of the biotic composition, size,
shape, connectedness, and cost of alternative reserve plans (Shafer
1990). When the geographic area is relatively small and the remaining
natural area limited to isolated patches in a matrix of cultivated
or intensively managed landscapes, the task of setting priorities
can be done through direct means such as rating the habitat islands
and selecting those with the highest ranking. TNC is faced with
setting priorities in some regions of the country that make the
problem more challenging, such as in the Columbia Plateau, which
is of large extent and is largely in natural or semi-natural condition.
The potential number of combinations of sites to represent all natural
communities in such a region is too large for simple ranking. In
addition, alternative sets of potential reserves need to be identified
that achieve a range of objectives. More sophisticated computer
models are required to find the sets of sites that meet the multiple
objectives involved.
Of the three
phases of reserve planning mentioned above, UCSB Biogeography Lab
(UCSB) has addressed the site selection task. TNC, as the decision
maker in this process, set its own goals and conservation targets.
The preserve design phase will require extensive field inventories,
detailed land use and economic analyses, land owner participation,
etc. and thus was beyond the scope of this project. A preserve selection
model originally developed for the Sierra Nevada Ecosystem Project
(Davis et al. 1996) was adapted to the task of selecting sites to
achieve alternative conservation strategies in the Columbia Plateau
ecoregion. It should be emphasized at the outset of the report that
the model is intended solely as an exploratory tool to evaluate
the implications of various policies and assembly rules. It does
not make the decisions. The TNC planning team developed all the
assembly rules and designed its recommended portfolio on conservation
sites. Our modeling activity only facilitated the rapid generation
and evaluation of alternatives and with identifying likely sites
to be considered for the portfolio.
As this is
a prototype of a new planning process, it is essential to leave
detailed tracks of the procedures used so that others may emulate
it elsewhere. This report attempts to document the portion of the
planning process undertaken in this research project. We begin with
a literature review of reserve selection models to convey the innovative
aspects of the approach used for the Columbia Plateau project. The
report continues with a thorough discussion of the process and of
the alternatives considered. The report then concludes with a set
of recommendations to TNC about implementing this approach in other
ecoregions.
Reserve selection
approaches
Conserving species and ecosystems in human-dominated environments
requires the maintenance of effective, representative systems
of biological reserves, combined with the judicious and sustainable
use of unreserved lands (Quinn and Karr 1993). Unfortunately, existing
reserves do not represent the full suite of native species and ecosystems,
most having been established on an ad hoc basis for reasons
other than their overall biotic composition (Pressey et al. 1993).
Selecting new reserve sites to improve biotic representation is
a complex process that must address multiple and often conflicting
biological, economic, and political goals. Biological goals include
adequate representation of environmental and biotic diversity, protection
of sensitive taxa or ecosystems, and conservation and restoration
of endangered taxa and habitats (Kershaw et al. 1995). The biological
value of candidate areas must be weighed against their cost in terms
of foregone economic opportunities, purchase, restoration and management
costs (Faith and Walker 1996). Similarly, there may be administrative,
regulatory, and other political considerations that favor some areas
over others (e.g., Reid and Murphy 1995).
In this report we are concerned with the site selection stage
as it might be practiced over thousands to tens of thousands of
square kilometers to satisfy conservation goals at the region, state,
or country level. Reserve planners have developed increasingly sophisticated
algorithms to provide more consistency and objectivity to the selection
process. One approach is to rate all candidate sites based on one
or more criteria and select those that score the highest (e.g.,
Lo et al. 1989). Scoring approaches are relatively straightforward
to implement using map weighting and overlay functions of Geographic
Information Systems (GIS). However, they cannot guarantee that all
biodiversity elements will be adequately represented, nor will they
guarantee and efficient allocation of resources (Pressey and Nicholls
1989). These goals are better addressed using "covering"
algorithms formulated to identify a minimal set of sites such that
each biological element is represented in at least one or more sites
(Pressey and Nicholls 1989, Underhill 1994). A related approach
that is also based on optimization theory maximizes biodiversity
representation in the set of sites that can be selected given a
fixed financial budget or total area (Kirkpatrick 1983, Margules
et al. 1988, Bedward et al. 1992, Church et al. 1996).
These site selection procedures implicitly address economic and
political costs by prioritizing sites, minimizing area, or maximizing
biodiversity representation. The optimization procedures in particular
seek to find efficient solutions for representing biological diversity
in a comprehensive reserve system (e.g., Kirkpatrick 1983, Margules
et al. 1988, Pressey and Nicholls 1989, Church et al. 1996), where
efficiency can be defined as the proportional number or area
of sites selected to represent all biotic elements to some required
level (Pressey et al. 1994). Optimization algorithms designed to
maximize efficiency alone provide solutions that are useful as benchmarks
for evaluating alternative proposals or existing ad hoc reserve
systems, but which are somewhat naive due to their single-minded
approach (Pressey et al. 1996). These algorithms may select a set
of sites that prove inferior once environmental, economic, and political
criteria are considered. Therefore, more complex models that explicitly
account for social or economic factors are needed to explore trade-offs
in planning to meet biodiversity conservation versus other social
goals or constraints.
THE COLUMBIA
PLATEAU ECOREGION
The Columbia
Plateau ecoregion (as delineated by The Nature Conservancy from
the map produced by the U. S. Forest Service [Bailey 1995]) encompasses
approximately 300,000 km² in portions of Washington, Oregon,
Idaho, Nevada, California, Utah, and Wyoming (Figure 1). The combination
of soils and climate generates a characteristic vegetation called
"sagebrush steppe", dominated by Artemisia spp.
or Atriplex confertifolia (shadscale) with short bunchgrasses
(e.g., Festuca spp., Pseudoroegneria spp.).
Figure 1.
Location of the Columbia Plateau Ecoregion
This ecoregion
was selected for development of a prototype ecoregional plan by
The Nature Conservancy for both practical and conservation reasons.
From a practical standpoint, the ecoregion was among the first for
which the requisite land-cover and land management mapping were
completed by the individual state-level GAP projects (Stoms et al.
in press, see also
the GAP web site for the Intermountain Semi-Desert Ecoregion).
Very little land in the ecoregion has been designated for maintenance
of biodiversity, while potentially conflicting land uses such as
grazing and cultivation are extensive. Enough undeveloped habitat
remains, however, for pro-active conservation action to be effective.
Thus the ecoregion makes a representative case study that could
be applied to other regions, particularly throughout the western
U. S. Planning for conservation and ecosystem management within
this ecoregion is also underway by the Oregon Biodiversity Project
(Vickerman 1996), and the Interior
Columbia Basin Ecosystem Management Project (ICBEMP, a joint
effort by the U. S. Forest Service and Bureau of Land Management,
Quigley et al. 1996). Proposals for new wilderness areas (Merrill
et al. 1995), national parks (Wright et al. 1994), and other core
reserves (DellaSala et al. 1996) are being discussed.
METHODS
Overview
of the planning process
Figure 2.
Flowchart of the planning process.
The planning
process is outlined in graphic form in seven steps in Figure 2.
The first step is to identify conservation goals, objectives, and
targets, which was done by the TNC planning team. UCSB conducted
steps 2-5 and assisted with part of the evaluation in step 6. In
step 2, the spatial data for the distribution of biodiversity elements
is summarized to determine which are already represented at or above
the predetermined goals of the alternative and which are still vulnerable.
For those that are underrepresented, the third step is to quantify
the area that is not currently protected for each element in each
planning unit. This task identifies the set of planning units that
are available to meet the representation goals. Step 4 calculates
a suitability index for each planning unit based on precomputed
attributes and a set of weights selected for each alternative. These
three steps are performed within a GIS, and the results are exported
as ASCII files which can be reformatted as input into an external
optimal site selection model at step 5. This model, described in
more detail in section 3.3 and Appendix
8.1, selects a set of planning units that satisfies the representation
goals with the best balance of efficiency (least area) and suitability
(best quality or most manageable sites). Data generated by the model
is returned to the GIS environment for further analysis and visualization.
The arrow from step 6 to step 1 emphasizes that this evaluation
can lead to refinements in the assembly rule specifications or to
fine-tune an alternative. Similarly, the process can be repeated
to test the sensitivity to different parameters such as the choice
of goals or the suitability factors. TNC then used the results of
the analysis to evaluate the set of sites of the preferred alternative
and adjusted the set of sites to design its recommended portfolio
as the final step. The process is described in greater detail through
the remainder of this section.
Planning steps
Identify
conservation goals, objectives and targets
The planning
process begins by setting conservation goals, objectives, and targets
for the plan. These can be stated as a set of preliminary decision
rules. Specifically, TNC decided what areas to consider currently
protected, what sites if any to be "core areas" that must
be in all alternative portfolio designs, what biodiversity elements
to represent in each alternative (i.e., the targets), and what representation
goals must be met for each target element. While this step in the
process was conducted by TNC, it is described here to clarify the
data used in the analysis and to define terms and concepts.
The first choice
is to determine what lands are already managed to protect biodiversity
and are thus a baseline in every alternative. Maps of land management
status were compiled for the Gap Analysis of the
Intermountain Semi-Desert Province (Stoms et al. in press),
which contains the Columbia Plateau ecoregion. TNC's definitions
are slightly different from GAP's, so the TNC team revised the original
GAP land management status map and updated missing or recently designated
management areas (Figure 3). All portfolio alternatives analyzed
in this report considered category I and II lands as existing biodiversity
management areas.
Category I:
Lands owned by private entities and managed for biodiversity conservation
or administered by public agencies and specially designated for
biodiversity conservation through legislative action where natural
disturbance events proceed without interference. Examples include
many TNC preserves and other private preserves committed to biodiversity
conservation and dedicated as state preserves or natural areas,
some national parks, some national wildlife refuges, federal wilderness
areas, some state parks and nature preserves.
Category II:
Lands generally managed for their natural values, but which may
receive use (e.g., habitat manipulation for game species) that degrades
the quality of natural communities. Also includes public lands for
with administrative designations for biodiversity conservation.
Examples include many national wildlife refuges, state wildlife
management areas, private preserves managed for game species, Bureau
of Land Management Areas of Critical Environmental Concern, federal
Research Natural Areas, etc.
Category III:
Lands maintained for multiple uses including consumptive or recreational
values and not specifically or wholly dedicated to biodiversity
conservation and lands with restricted development rights. Examples
include Department of Agriculture Forest Service and BLM, Department
of Defense buffer lands, state forests, regional and large local
parks and open space, private lands protected from subdivision by
conservation easements or other title restrictions, etc.
Category IV:
Lands with no known protection, including lands used for intensive
human activity, and agricultural, residential, and urban lands,
public buildings and grounds, transportation corridors, etc.
Figure 3.
Management status in the Columbia Plateau Ecoregion
In conservation
planning, some sites may be so obviously valuable for protection
that they can be considered as core areas in any alternative. One
popular means of doing this is to convene a workshop of experts
to identify the best remaining examples of rare elements. TNC held
a workshop, organized along taxonomic lines, in January, 1997. Six
panels developed their own set of priority sites. TNC then identified
the corresponding subwatersheds that contained these sites and summed
the number of workshops that concurred on its importance. The TNC
planning team decided that, as a second decision rule, the 105 subwatersheds
identified by at least four expert workshops would be allocated
as core biodiversity management areas in all portfolio alternatives.
(The maximum number of workshops that identified any subwatershed
was five). Core areas were combined with existing BMAs to determine
remaining vulnerability.
Once the decision
rules are established for what lands are or must be protected, the
next rule determines what elements of biodiversity should the portfolio
represent. The TNC team identified two classes of target elements:
vegetation alliances (coarse-filter) and rare elements (fine-filter)
from the Natural Heritage databases. Alliances had been mapped for
each of the states in the ecoregion using Landsat satellite imagery
from 1990 (+/- 2 yrs) and then combined into a regional land-cover
map for the gap analysis of the Intermountain Semi-Desert Province
(Stoms et al. in press). The smallest land-cover feature mapped
is 100 hectares or one square kilometer. Land-cover was classified
by alliances (characterized by a diagnostic species of the uppermost
stratum of the canopy) or to groups of closely related alliances.
The TNC core
team used rare element occurrence data from the various state Natural
Heritage programs and state fish and wildlife departments as the
fine-filter targets. For vertebrates and invertebrates, the team
flagged occurrences to be considered for representation. All plants
with global ranking of G1, G2, or G3 were to be represented. For
plant associations, only for G1 and G2 communities were picked as
targets.
Representation
goals for the target biodiversity elements were set as the minimum
area or number of occurrences that alternatives must achieve. These
goals are estimates of the importance of each element and the amount
needed to maintain viable populations. As the TNC core planning
team developed representation goals for the coarse-filter alliances
as surrogates for other biodiversity elements, they had two implicit
goals: to capture not only the cover types but also their range
of environmental variability, and to ensure that rare types endemic
to this ecoregion were give special attention. Based on these goals,
they ranked the cover types by their overall regional distribution,
their value as a coarse-filter to cover the plant associations within
them, their relative rarity, and their pattern of distribution (i.e.,
primarily large or small patches). Five groups of cover types were
categorized as follows (assignment of cover types to representation
goals are shown in Table 1):
Group A:
Those which have high or medium coarse filter value, and typically
occur in small patches in the landscape. Most of these are restricted
to unusual substrate or hydrologic conditions (or maybe even disturbance
regimes), and/or are limited in their distribution and so need to
be protected in the Columbia Plateau ecoregion. The representation
goal should center around capturing 50% of the area of these
cover types within each section in the ecoregion if the total area
in this ecoregion is small (i.e., < 500 km²). For cover
types of greater extent, the goal was set at 25%.
Group B:
Those which have medium coarse filter value and occur in relatively
small patches. This is an interesting group of alliances, and contains
two different patterns of vegetation types: those that are more
characteristic of neighboring ecoregions but nevertheless have relatively
large disjunct areas and are important within the Columbia Plateau
(e.g., some of the forest types on isolated mountain ranges); and
some of the less common Artemisia alliances with limited
ranges of distribution. Most of these have total areas of < 500
km². The goal for this group was set at 20% representation
within each section the type occurs in.
Group C:
All those with high to medium coarse filter value and typically
found in big patches. This includes the vegetation types that really
"distinguish" the Columbia Plateau from surrounding mountainous
ecoregions: Juniperus Woodlands, Artemisia shrublands,
big sage - low sage, Atriplex salt desert, perennial grasslands.
Most of these are very heterogeneous containing many associations.
Several of them cover >10,000 km² and all are over 1000
km² in size within the ecoregion. The representation goal was
set at 10% within each section.
Group D:
Those which have low coarse filter value and which are mostly in
small patches. These are primarily vegetation types which are only
peripherally in the ecoregion because of the vagaries of the boundaries.
Their primary range of distribution is outside of this ecoregion,
and so most protection will not occur in the Columbia Plateau. These
cover types were assigned no representation goal.
Group E:
Cover types or land uses of no conservation interest, such as developed
and cultivated lands and exotic or planted grasslands. Water bodies
were included only because aquatic features are not well mapped
at the regional scale of Gap Analysis. This group also had no representation
goal.
Table 1.
Representation goals for land-cover types
Land-cover
type |
Mapped
distribution (km²) |
Group
A coarse-filter < 500 km² (50% goal) |
Seasonally/temporarily
flooded cold-deciduous forest |
382 |
Populus
tremuloides woodland |
184 |
Quercus
garryana woodland |
463 |
Non-tidal
temperate or subpolar hydromorphic rooted vegetation (marsh
and wetland) |
482 |
Sparsely
vegetated sand dunes |
345 |
Sparsely
vegetated boulder, gravel, cobble, talus rock |
69 |
|
|
Group
A coarse-filter > 500 km² (50% goal) |
Pinus
ponderosa woodland |
5,804 |
Artemisia
rigida dwarf shrubland |
700 |
Temperate
deciduous shrub types--Mountain brush |
2,027 |
Cercocarpus
ledifolius or C. montanus shrubland |
516 |
Purshia
tridentata shrubland |
1,140 |
Seasonally/temporarily
flooded cold-deciduous shrubland |
1,279 |
Sarcobatus
vermiculatus shrubland |
3,576 |
Seasonally/temporarily
flooded sand flats |
1,670 |
|
|
Group
B small patch communities (20% goal) |
Abies
species (A. concolor, A. grandis or A. magnifica)
forest or woodland |
1,397 |
Picea
engelmannii and/or Abies lasiocarpa forest or woodland |
83 |
Pseudotsuga
menziesii forest |
2,149 |
Populus
tremuloides forest |
740 |
Pinyon
woodland (Pinus edulis or P. monophylla) |
165 |
Pinyon-juniper
woodland (Pinus edulis or P. monophylla with Juniperus
osteosperma or J. scopulorum) |
193 |
Pseudotsuga
menziesii woodland |
27 |
Artemisia
cana shrubland |
536 |
Artemisia
tripartita shrubland |
3,696 |
Artemisia
nova dwarf-shrubland |
164 |
|
|
Group
C large patch communities (10% goal) |
Juniper
woodland (Juniperus osteosperma or J. scopulorum) |
2,101 |
Juniperus
occidentalis woodland |
18,380 |
Artemisia
tridentata ssp. vaseyana shrubland |
17,181 |
Artemisia
arbuscula-A. nova dwarf-shrubland |
1,816 |
Artemisia
tridentata-A. arbuscula shrubland |
45,144 |
Artemisia
tridentata shrubland |
64,574 |
Mixed
salt desert shrub (Atriplex spp.) |
11,304 |
Dry
grassland Pseudoroegneria (Agropyron)-Poa |
15,671 |
Moist
grassland Festuca |
2,671 |
|
|
Group
D peripheral communities (0% goal) |
Pinus
contorta forest |
176 |
Pinus
ponderosa forest |
153 |
Pinus
ponderosa-Pseudotsuga menziesii forest |
784 |
Pinus
monticola-Thuja plicata forest |
20 |
Pinus
flexilis or P. albicaulis woodland |
104 |
Pinus
contorta woodland |
22 |
Pinus
jeffreyi forest and woodland |
2 |
Alpine
tundra |
3 |
Wet
or dry meadow |
30 |
|
|
Group
E cultivated, developed types and water (0% goal) |
Agropyron
cristatum seedings, Poa pratensis, hayfields, and
Conservation Reserve Program lands |
8,169 |
Annual
grasses Bromus tectorum, etc. |
10.177 |
Urban
or human settlements and mining |
1,201 |
Agriculture |
69,820 |
Water |
3,568 |
The TNC core
team also set representation goals for the rare element occurrences.
For every rare vertebrate and invertebrate, the team set a goal
of five representations (i.e., subwatersheds) for each in every
section it occurred in. If there were less than five mapped occurrences
in a section, then the goal was to represent all occurrences for
that species. The goal for plant species was similar with three
exceptions. Plants in the Palouse section (331A in Figure
1) were to have up to seven representations because this particular
section has been the most heavily impacted by cultivation. Third,
plants identified by the core team as endemic to a single section
were also given a goal of up to seven representations. For plant
associations, the goal was for up to five representations per section.
For modeling purposes, an area of 1000 ha was arbitrarily assigned
to each subwatershed in which target elements occurred, and the
goal was set to 1000 times the number of required occurrences. In
essence this amounts to adapting the BMAS model to a covering problem
where each element needs to be covered n times.
Determining
remaining vulnerability and unprotected area
Vulnerability
was determined by comparing the spatial extent of each target element
within existing BMAs (category I and II lands) plus core areas with
the area required to meet the representation goal within each section
of the ecoregion (step 2 in Figure 2). If an element falls short
of the representation goal, its remaining vulnerability is calculated
as the difference in area. For vulnerable elements, data are summarized
on the extent of the element for each planning unit (i.e., subwatershed)
in the section (Step 3 in Figure 2). These two steps are performed
by GIS overlay of the maps of land management and subwatersheds
with the maps of the target elements. (See also
Appendix 8.3 for the ARC/INFO AML used to process the GIS files
to measure vulnerability and generate the data files for the BMAS
model.
Map suitability
of planning units
TNC needed
a means of integrating programmatic, economic, and socio-political
factors into the portfolio design process (Question #3 in the Introduction,
McKendry and Machlis 1991). These factors can be collectively termed
as "suitability." Mapping the suitability of landscapes
for various uses has been a cornerstone of planning since the technique
was popularized by Ian McHarg (1969). Factors known to constrain
or facilitate a specific land use are overlaid to derive a site
suitability map. Therefore, a set of attributes were generated for
each planning unit (i.e., the HUC6 subwatersheds) to provide measures
of habitat condition, site manageability, and spatial factors (Table
2). The index is scaled such that high values are the least suitable,
which is required to maintain the minimization objective of the
BMAS model.
Table 2.
Suitability factors used in evaluating and selecting alternative
portfolios of sites
Roadedness:
The presence of roads creates negative impacts on many species,
including fragmentation, noise, edge effects, hunting pressure,
predation by pets, spread of disease, and invasion of exotic pests.
An index of roadedness can provide a useful indicator of habitat
condition. Road data were obtained from the ICBEMP project, which
had processed the 1990 census TIGER files. Additional 1:100,000
scale data for some Nevada counties outside the ICBEMP study area
were obtained directly from the TIGER CD-ROMs and processed in a
similar manner. The road arcs were buffered with a buffer width
related to the class of road. This buffer operation was used to
estimate the area of land actually impacted by the presence of each
road, where freeways were assumed to affect a greater spatial extent
than dirt roads. This operation also accounted for the spatial distribution
of roads which a simple measure of road density (i.e., km of road
length per km² of area) does not. For instance, urban streets
could total a long length but because they are so closely spaced,
they do not affect as large an area of habitat as a similar length
of road spread uniformly across a subwatershed. The roadedness index
was calculated by summing the total area of buffered roads per subwatershed
and converting the area to a percentage of subwatershed area. Values
ranged from roadless (i.e., index = 0) to fully roaded (index =
100), with a mean value of 15.95%.
Human Population
Density: The presence of large numbers of people represents
similar impacts as roadedness and may also indicate higher land
values. Population data from the 1990 census data were obtained
from CIESIN [ftp://ftp.ciesin.org/pub/census/usa/grid/pall/us/].
CIESIN had interpolated block group data to a 1 km lattice of the
United States. We converted this lattice into a grid and cropped
it to the ecoregion. By summing population from all the cells in
a subwatershed and dividing by subwatershed area, an estimate of
population density was derived. Values ranged from 0 to 1710.5 people
per km2 (regional mean of 5.2), with high values indicating urbanized
subwatersheds that would generally be unsuitable for protection
of most forms of native biodiversity.
Expert Opinion:
In addition to their use in picking core areas, the number of concurring
expert workshops for the remaining subwatersheds (i.e., values from
0-3) was used as another suitability factor, where higher values
were presumed to be more suitable. The mean value for these non-core
areas was 0.8.
Aquatic
Integrity Index: As most of the suitability factors addressed
terrestrial habitats, the planning team chose this index developed
by the ICBEMP project to represent suitability of aquatic habitats.
The index was computed for the HUC5 watersheds (one level above
subwatersheds in the hierarchy) as a mean of three component indices
of fish community integrity. The three components were a relative
index of strong populations of key salmonids, a relative index of
the ratio of native species diversity to total species diversity
multiplied by native species evenness, and a relative index of species
richness within the parent subbasin. The minimum value of this index
was 0 for very poor sites and 0.883 for the best watersheds, with
a regional mean of 0.326.
Percent
of Land in Private Ownership: The cost of changing land management
to better protect the long-term viability of native biodiversity
is partly a function of current land ownership and management. Therefore
we have included an index of the proportion of land in a subwatershed
that is in private ownership (either individuals or corporations),
derived from the land ownership/management coverage developed for
Gap Analysis (Stoms et al. in press). Values ranged from 0 to 100
percent (regional mean of 46.3), with high values indicating watersheds
with high probable costs for management of biodiversity.
Percent
of Land Converted to Human Uses: As landscapes become more modified
by human actions, it becomes more difficult to maintain large-scale
ecological processes needed to sustain ecosystems. The GAP land-cover
map was reclassified into native communities and human modified
cover types. The latter included developed and agricultural lands,
exotic annual grasslands, and seedings of crested wheatgrass. The
areal extent of human land use types was summed by subwatershed
and then divided by its area to convert to a percentage. Values
ranged from 0 to 100 (mean of 29.6), with high values of the index
indicating highly modified landscapes.
Distance
from Existing BMA or Core Area: The current BMAS model has no
explicit mechanism for considering spatial relations in selecting
a set of planning units. To satisfy the core team's desire to achieve
some level of clustering of selected units, the distance from "seed
areas", which were the existing BMAs and the core areas (which
were identified by at least four expert panels), was added as another
suitability factor. Thus planning units nearer these seed areas
had a lower factor score and hence were considered more suitable
than those farther away. An ARC/INFO GRID was created with the existing
BMAs plus the core areas with a 1 km cell size. A distance grid
was generated using the EUCDISTANCE command in GRID, assigning a
value to each cell. Then the ZONALSTATS command with the MIN option
was used to determine the minimum distance value among the cells
in each planning unit to the nearest seed area. The distance values
range from 0 (adjacent planning units) to 109.55 km, with a regional
mean of 18.18 km.
Figure 4.
Map of individual suitability factors (Darker areas are least
suitable)
Figure 4.
Map of individual suitability factors (continued) (Darker areas
are least suitable)
The suitability
factors may seem redundant, representing similar positive and negative
socio-political, economic, or environmental aspects of planning
units. Nevertheless they are generally not highly correlated (Table
3). The highest correlation between pairs of factors was 0.52 between
percent private land and percent converted land. Roadedness and
percent converted had a correlation of 0.41, while that for roadedness
and percent private was 0.37. Population density was not as correlated
with roadedness as one might expect, with a correlation value of
only 0.27. The aquatic integrity index and the number of expert
panels generally had small negative correlations with the socio-political
factors. Distance from core area was extremely correlated with the
overall suitability index because the distance index was weighted
so highly in calculating suitability.
Table 3.
Correlation matrix and weights of suitability factors
|
Default
Weight |
Population
Density |
#
Expert Panels |
Aquatic
Integrity |
%
Private Land |
%
Converted |
Distance
from Core |
Overall
Suitability |
Roadedness |
0.2 |
0.27 |
-0.15 |
-0.06 |
0.37 |
0.41 |
-0.01 |
0.12 |
Population
Density |
0.2 |
|
-0.04 |
0.04 |
0.12 |
0.17 |
0.01 |
0.30 |
#
Expert Panels |
0.2 |
|
|
0.04 |
-0.17 |
-0.19 |
-0.32 |
-0.37 |
Aquatic
Integrity |
0.2 |
|
|
|
-0.12 |
0.004 |
-0.12 |
-0.11 |
%
Private Land |
0.2 |
|
|
|
|
0.52 |
0.16 |
0.24 |
%
Converted |
0.2 |
|
|
|
|
|
0.08 |
0.20 |
Distance
from Core |
5.0 |
|
|
|
|
|
|
0.95 |
Figure 5.
Map of the overall suitability index based on default weights
For each alternative,
an overall suitability index would be computed as the weighted sum
of the individual factor values. Weighting was a two-step process.
First each factor was divided by its regional mean value to normalize
the values. Then a set of weights were chosen to emphasize specific
factors. A weight could be set to zero if the factor was to be ignored
in a particular alternative. The default set of weights, shown in
Table 3, gave greatest emphasis on the distance to core areas, which
had the effect of clustering selected sites near these core areas.
The suitability factors are displayed individually in Figure 4 and
as an integrated suitability index based on the default weights
in Figure 5.
Sites with
low suitability (i.e., a high index value) were not automatically
excluded from consideration in selecting alternative portfolios.
Such sites may contain irreplaceable biodiversity elements that
must be in the portfolio in order to achieve representation goals.
If there is a choice of sites to represent the same element, those
that are most suitable will be selected.
Select
planning units with the BMAS model
It is necessary
to select enough area for biodiversity management options that we
keep elements from being considered vulnerable. Since we might consider
hundreds of elements to be vulnerable and we can select from among
hundreds of planning units for targeted action, the problem is relatively
complex. We can represent this decision problem as an integer-linear
programming model where the objective is to optimize the selection
of suitable areas for biodiversity management such that enough area
is selected for each element to keep it from being considered vulnerable.
We have formulated an optimal siting model that addresses trade-offs
between the efficiency and the overall suitability of reserve systems.
Each planning unit is described by its area, its biological properties,
and by non-biological properties that make it more or less suitable
for conservation management. Our multi-objective model, which we
have dubbed the Biodiversity Management Area Selection (BMAS) model,
selects sites to meet the predefined representation goals while
balancing the dual objectives of efficiency and suitability. Either
a planning unit is selected as a BMA or it is isn't. This is enforced
by the definition of the integer decision variables and is formalized
in constraints. Further details about the formulation of the model
can be found in Davis et al. (1996) and in Appendix
8.1.
Unfortunately,
the BMAS model is related to the class of n-p hard problems that
can be found in the integer programming literature (like the travelling
salesman problem). Basically, worst-case instances of large n-p
hard problems may require an inordinate amount of computer time
to solve optimally. Consequently, much of our past research has
been focused on the design of a robust heuristic to solve the BMAS
problem. Our heuristic is based on the combination of several well-known
methods including greedy, interchange, and multiple drops and adds
(which represents a form of strategic oscillation). In testing the
heuristic against known bounded solutions for selected problems,
heuristic performance was consistently within 2% of optimality.
The details of the approach are given in Okin (1997).
Evaluate
selected set of planning units
The BMAS model
operates independently from the GIS database so the output report
(see an example in Appendix 8.4.1)
can only contain information about which planning units were selected
and some summary information about total area and cumulative suitability.
The identity of selected sites is therefore imported back into the
GIS environment for additional spatial analysis and visualization.
(The ARC/INFO AML and awk scripts are listed in Appendix
8.4). Once in the GIS environment, the BMAS solution for the
alternative can be portrayed as a map, making it easier for analysts
to evaluate the sites in relation to other GIS data (e.g., land
ownership or management) or with their personal knowledge about
individual sites. To facilitate the evaluation, another program
(Appendix 8.5) was written to
summarize all GIS data about any individual subwatershed, i.e.,
target elements it contains, its management and ownership, and its
suitability factors. This evaluation can lead to modification of
the initial decision rules and generation of new alternatives or
to refinement of the set of sites in the recommended portfolio (step
7). The TNC planning team conducted this part of the evaluation.
Another aspect
of evaluation is to determine how efficient an alternative is, based
on one set of biodiversity elements, at meeting the representation
goals for another set. This coincidental representation of one set
of targets by another has been termed "sweep analysis"
(Kiester et al. 1995). We used sweep analysis to evaluate how many
of the vegetation alliances and rare elements would be swept, or
represented by, the set of conservation planning units identified
in the expert workshops. The planning units picked by five workshops
were assigned as core areas and the number of elements not swept
by them was determined by repeating planning step 2 to calculate
their remaining vulnerability. This process was repeated for planning
units identified by at least four workshops, three, two, and one.
TNC was also
interested in a policy question about the role of public and private
lands in a potential portfolio. Given that conservation strategy
is often more feasible to implement on public land, TNC asked whether
the region was flexible enough to allocate a greater share of the
sites in the BMAS solution from public lands. And if this were possible,
what was the trade-off in efficiency? To answer this questions,
the suitability index was modified from the baseline alternative
by giving much greater weight (5.0 vs. 0.2) to the percentage of
private lands in a subwatershed. The BMAS model was run for this
variation (with all other parameters held the same) and the spatial
pattern of the two alternatives were compared, along with their
respective proportions of public and private lands. Because the
suitability index is calculated differently in the two alternatives,
it is not possible to compare cumulative suitability.
Alternatives
considered
By modifying the set of conservation targets, representation goals,
existing biodiversity management areas, and suitability weights,
any number of alternative portfolios can be generated. As this modeling
approach is exploratory and since there were no requirements for
a full range of alternatives as would be the case for a federal
land management decision, a relatively small set of alternatives
were generated in this study by varying the decision rules. Alternatives
were generated for the land-cover types alone (coarse-filter), rare
elements alone (fine-filter), and both cover types and rare elements
together (integrated coarse- and fine-filters). The representation
goals were fixed as described in section 3.2.1, with the default
suitability weights. These goals applied to each of the seven sections
of the ecoregion. Managed areas in categories I and II were assumed
to be protected in all alternatives. All subwatersheds identified
by at least four of the six expert workshops were automatically
included in every alternative as core areas. Thus for each alternative,
there were three types of site in the portfolio: existing reserves,
core areas from the experts, and additional subwatersheds selected
in the BMAS model to achieve the representation targets.
Next Section
Return to Table of Contents
Biogeography Lab Home Page