Spatial Decision Support Systems
For Wildlife Conservation Planning
Frank W. Davis
David Stoms
Biogeography Lab
Donald Bren School of Environmental Science and Management
University of California
Santa Barbara CA USA 93106-5131
email: fd@bren.ucsb.edu
1
Summary
This paper reviews recent developments in the application of information system
technologies and decision science to land and water use planning to conserve wildlife
species and their habitats. We describe two applications of SDSS to the problem of
conservation planning at the regional level in California. The first example involves the
use of a hierarchical knowledge base linked to a GIS to evaluate potential nature reserve
sites for the University of California’s Natural Reserve System. The second example is
also from California and involves identifying a set of potential reserves for protecting
terrestrial plant communities and wildlife habitats in the northern Sierra Nevada region.
These examples illustrate thr kinds of different tools and approaches that can assist
regional land planners to incorporate biodiversity data and information for siting
conservation and restoration projects.
2
The Need for Integrated Land Planning
In many parts of the world, there are intense conflicting demands for rural lands to
provide space for new residential and industrial development, to produce more food and
fiber, and to maintain natural areas for open space, recreation, watershed protection and
protection of native plant and animal species. Rural land planning has taken on a new
urgency as our rapidly growing human population demands ever more from a finite land
base.
Land-use planning is “ the systematic assessment of land and water potential, alternatives
for land use and economic and social conditions in order to select and adopt the best
land-use options” (FAO 1993). Land planning occurs at many different levels from
national to regional (e.g., state, district, province) to local (e.g. county, community,
township, village). Although some form of land planning has been practiced for much of
human history, systematic planning to conserve biological diversity through strategic
allocation of reserve land is relatively recent. Instead, biodiversity conservation has been
an ad hoc process that has resulted in inefficient and ineffective systems of nature reserves
and poorly coordinated conservation activities. However, over the past decade the trend
in conservation planning has been from ad hoc to more systematic approaches to
prioritizing conservation needs and siting new reserves (Scott, Davis et al. 1993; Margules
and Pressey 2000).
At present systematic conservation planning is not well integrated into general land use
planning and decision making, either in the United States or elsewhere (Pressey 1999).
Conservation planning tends to be conducted by conservation biologists and wildlife
ecologists who are focused on siting reserves. In contrast, most local and regional
planning departments are staffed with urban and regional planners who are focused on
the complementary problem of planning new land development and associated public
infrastructure (roads, water supply, etc.). Until recently, even natural resource planners in
governmental agencies have not been concerned with reserve planning, but have instead
focused on harvest scheduling to maintain sustainable yield and on siting of activities to
avoid or minimize impact on biologically sensitive areas. Whereas conservation planners
have focused almost exclusively on biological values, both urban planners and natural
1
resource planners have tended to focus on social values of reserves such as scenic quality,
recreation, water supply or flood control.
Conservation planners, urban and regional planners, and natural resource planners have
each developed their own theory, language, methods and tools. They have also tended to
operate over different geographic scales. Reserve system planning has typically been
conducted at national to regional levels with coarse data. Urban and regional planning
has tended to operate at regional to local to levels using moderately fine data. Natural
resource planning has often operated at still finer scales of local administrative units (e.g.,
a district or individual park) using site-level information.
There is an obvious need to bring together the perspectives of urban and regional
planners, natural resource planners, and conservation planners in order to better avoid
and resolve conflicts over land zoning and allocation decisions (Forman and Collinge,
1997 ). This kind of integrated land planning is an extremely complex undertaking,
characterized by
 Multiple, often conflicting goals,
 Imprecisely defined objectives,
 Very incomplete information,
 A very large number of feasible alternative solutions.
Over the past 25 years new analytical approaches and tools have been devised to help
land planners identify, evaluate and select among alternatives. Now a great deal of
research and and development effort is focused on creating spatial decision support
systems (SDSS) that couple the analytical power of multicriteria decision methods and
dynamic simulation models with Geographic Information Systems (GIS) for land use
planning (Bantayan and Bishop 1998; Grabaum and Meyer 1998; Matthews, Sibbald et al.
1999). At present we do not have robust SDSS that brings together the tools and
information of urban and regional planning, natural resource planning and conservation
planning. However, good progress has been made in each of these areas and we can
anticipate the appearance of such tools within the next few years.
In this paper we describe three applications of SDSS to the problem of conservation
planning at the regional level. The first example involves the use of a hierarchical
knowledge base linked to a GIS to evaluate potential nature reserve sites for the
University of California’s Natural Reserve System. This example illustrates multi-scale
land planning using a formal multi-criteria scoring approach that employs fuzzy
knowledge bases.
The second example is also from California and involves identifying a set of potential
reserves for protecting terrestrial wildlife in the northern Sierra Nevada region. This
example illustrates how SDSS can be used to design a representative reserve network by
linking a heuristic search procedure known as simulated annealing to GIS data about
biological and social conditions.
2
3
Application of a Fuzzy Knowledge Base to Site a new Scientific
Reserve
The IUCN Commission on National Parks and Protected Areas classifies nature reserves
designated for scientific research, education, and environmental monitoring as “scientific
reserves” (IUCN Commission on National Parks and Protected Areas, 1994). In
the United States, examples of such reserves include the Long-Term Ecological Research
network funded by the National Science Foundation (Franklin, 1990), the Man and the
Biosphere program (Batisse, 1982), research natural area (RNA) programs of several
federal agencies and the University of California Natural Reserve System (UC-NRS)
(Ford, 1988). The UC Reserve System is the world’s most extensive University system
of scientific reserves and is comprised of 35 natural reserves affiliated with its nine
campuses.
Although programs such as the RNA and UC-NRS have developed qualitative criteria
for evaluating the suitability of sites as research reserves, they generally lack an explicit,
operational procedure for comparing candidate sites (Stoms et al., 1998) In planning for
a new University of California (UC) campus near Merced, California, UC is considering
establishing one or more additional NRS research and teaching reserves in the
neighboring Sierra Nevada or the San Joaquin Valley. To support this planning process,
we developed a multi-scale, “top-down” decision support tool for selecting new sites to
expand the NRS based on University guidelines. The tool was then applied specifically to
assess site suitability for establishing an NRS reserve in a specific ecological system
associated with the Central Valley of California (vernal pools and grassland ecosystems).
The study is only briefly summarized here. Details are available in Stoms et al. and online
at http://www.biogeog.ucsb.edu/projects/snner/nrs_report.pdf.
To address the lack of detailed site-level information across the entire planning region,
we developed a three-staged assessment process involving the use of relatively coarse
data to successively screen the set of candidate sites in the first two stages before
preparing a more detailed assessment of finalist sites in Stage 3. At each stage, the general
procedure was to:
1) delineate a “planning region” that encompassed all possible areas for the new
reserve;
2) divide the planning region into non-overlapping “planning units” that were
scored for their suitability as candidate NRS reserves.
3) compile new and existing data about each planning unit that served as evidence
for evaluating the suitability of the area;
4) combine geographical evidence for each planning unit using a fuzzy logic
network model to generate a “suitability” score for that unit. The Ecosystem
Management Decision Support (EMDS) software was used to create and
visualize the fuzzy logic network model.
3
3.1
Modeling site suitability using Fuzzy Knowledge Bases
GIS-based land suitability analysis has been in increasing use ever since Ian McHarg first
promoted the practice over thirty years ago (McHarg 1969). In a general sense, land
suitability analyses involves weighting and combining criteria to determine the fitness of a
specific place for specific uses based on the observed characteristic of the place and on
the values of land users.
A number of approaches have been developed for multicriteria evaluation of land
suitability. These range from simple weighted linear combinations of maps [Pereira,
1993 #947] to hierarchical weighted summation using procedures such as the Analytical
Hierarchy Process (Saaty 1980) or Simple Multiattribute Rating Theory (Rothley 1999), to
Boolean algebra in which in which sites are screened through a series of logical filters
(Hall, 1992). Geographic information systems (GIS) serve these multi-criteria
evaluation approaches well, providing the attribute values for each location and both the
arithmetic and logical operators for combining attributes (Jiang and Eastman, 2000).
Fuzzy logic has been effectively applied as an alternative to Boolean logic, weighted linear
combination, maximum limitation, and other methods of suitability assessment in a
number of recent applications (e.g., Liang and Wang, 1991; Hall et al, 1992;
Charnpratheep, 1997). Fuzzy methods apply a measure of the degree of membership
in a fuzzy set, such that a factor can be partly true. The approach uses Boolean operators
such as AND and OR, for combining factors in a multi-criteria evaluation (Reynolds et
al., 2000). Expert knowledge is required to represent the logic of suitability assessment
in a given domain, but the formal logic representation makes the process explicit and
transparent. This can be especially important in land planning where the goals and values
of different stakeholder groups may conflict.
There are no standards for rating site value as a scientific reserve. Instead, evaluation
requires expert judgement in considering many different, often imprecisely-defined,
criteria. Thus the problem is especially well suited to analysis by fuzzy knowledge base
analysis. Bourgeron et al. (2000) developed a fuzzy knowledge base to assess land
suitability for conservation reserves. Our analysis is similar to theirs as an exercise in
conservation planning; however in addition to biodiversity conservation goals we are also
concerned with academic and administrative goals that are associated with scientific
research reserves.
The task of assessing the suitability of sites as potential new UC reserves was undertaken
using the Ecosystem Management Decision Support (EMDS) system from the U. S.
Forest Service (Reynolds et al., 2000). EMDS consists of three components: a
knowledge base development tool (Netweaver), a GIS application framework, and an
assessment system. Netweaver allows developers to encapsulate knowledge about the
system of interest, in this case the characteristics of a good research and teaching reserve
according to the UC guidelines. It allows the analyst to build the hierarchy of networks
of propositions using graphical tools, similar to spatial influence diagrams (Zhu and
others 1998). The assessment system enables the end-user to evaluate the knowledge
base for a specific spatial database and to display and interact with the results in the GIS
environment.
4
3.2
The three-stage evaluation process
The University of California’s Natural Reserve System (NRS) employs a set of guidelines
for evaluating and selecting new reserves. There are three general categories of criteria—
scientific, academic, and administrative. Scientific criteria address the biological
significance of the site as well as the integrity (“viability”) of its ecosystems. Academic
criteria include the number of disciplines that could use the site for teaching or research
and the accessibility from the campus for those purposes. The third category deals with
administrative criteria of filling “gaps” in representation of California’s natural
ecosystems and the costs and manageability of the site. To be rated as highly suitable, a
site must receive relatively high scores for all three suitability criteria.
Our 3-stage evaluation process began with a coarse regional screening of a 63,000 km2
planning region defined by the San Joaquin Valley and Southern Sierra (Figure 1, Table
1). We divided the region into 1,400 relatively large planning units that ranged in size
from 3,300 – 9,400 ha. Planning units were evaluated for their suitability using
information on ecological condition (e.g., level of fragmentation by roads), richness of
rare and endangered species, proximity to the Merced campus site, and the
complementarity to (i.e., overall dissimilarity from) existing UC reserves in terms of
biophysical environments. Planning unit scores ranged from very low to moderately high,
with much of the variation due to travel time from the campus and the presence of rare
and threatened species.
In Stage 2, we focused on a relatively high-scoring area that was 20% of the original
planning region using smaller planning units defined mainly by ownerhip boundaries and
roads. We searched specifically for sites that contained vernal pool ecosystems that are
characteristic of the region surrounding the Merced campus site. Vernal pools are
shallow, temporary wetlands that are a distinctive feature of California supporting many
rare, threatened and endangered plant and animal species (Mead 1996). Vernal pools are
considered one of the most threatened ecosystems in California, with a significant
proportion of their distribution lost to cultivation or urbanization. In Stage 2 we also
began to examine evidence indicating favorable conditions for land purchase and
management such as the number of land owners in the planning unit (Figures 2 and 3).
In Stage 3 we focused in on the planning units that rated highest in Stage 2 (Figure 3),
split them into smaller units, and examined in still more detail their scientific, academic,
and administrative ecological characteristics. Here we began to compile evidence about
local conditions such as local habitat diversity, watershed integrity, current land zoning,
local road access and condition of existing infrastructure. This kind of information would
have been very time difficult to collect over the entire Stage 1 planning region. For the
small Stage 3 planning region we were able to use recent maps, large-scale air photos, and
field surveys. The resulting map of scores identified a set of parcels including and to the
east of the planned site for the campus that would appear to have exceptionally high
value for a scientific reserve.
During the time of our analysis the campus development site has been re-evaluated in the
light of its biological value, and the campus has been moved onto a neighboring, less
sensitive area. At the same time, funds have been contributed by the David and Lucille
5
Packard Foundation to acquire and protect the original campus site and surrounding
lands as a biological reserve.
3.3
Strengths and limitations of the procedure
The dilemma of spatial extent of the assessment region versus consistency and detail of
information about the assessment units was addressed by designing a hierarchical threestage process. At each stage, the highest resolution data that were comprehensive for the
extent of the assessment region were utilized. The finest resolution data were only
required for a relatively small area for which it is more practical to compile. In this
manner, we were able to identify a relatively few highly suitable parcels within a total
region of 63,000 km².
The 3-stage combination of fuzzy knowledge bases proved to be a highly effective way to
measure site suitability. We were able to capture general criteria and their relationships in
an explicit form that can be critiqued and continually updated as better ecological
understanding and data emerge. The process of translating the guidelines into a
knowledge base structure also helped identify weaknesses in the current guidelines.
Fuzzy logic was designed specifically to cope with linguistically imprecise factors. In
addition, it casts all factors into a common range of truth (or membership) values. This
assignment of membership has great flexibility, accommodating non-linear relationships,
Boolean values, and weighted linear combinations of factors. Multicriteria suitability
assessments often have criteria that compete with one another. Fuzzy logic provides
formal mathematical operations to handle combinations of factors. Analysts can quickly
try alternative assessments and visualize the results of the overall network or any
individual subnetwork.
There are several limitations to the approach described here. Many of the data sets used
were of unknown accuracy and we have not conducted a detailed ground assessment to
validate our results. The knowledge base may create a false sense of “objective scientific
rigor” in providing quantitative measures for what were in fact qualitative and necessarily
somewhat subjective evaluations. Even using fuzzy membership functions, the use of
fuzzy “AND” operators in several parts of the Knowledge Base is very restrictive and we
may have under-estimated the value of sites that were exceptionally strong in some
criteria but were weaker in just one or a few others. Other approaches such as the Simple
Multiattribute Rating Technique and the Analytical Hierachy Process, which use
weighted summing of scores rather than Boolean operators, may be preferable where one
is seeking to balance different perhaps conflicting criteria rather than strictly seeking sites
that must score highly in all criteria.
In this exercise we were seeking the best single site for a scientific reserve, however we
did not consider whether the campus needs could be better served by two or more
smaller and perhaps complementary sites (e.g., one site close to campus and another
more diverse and pristine site at greater distance.) Scoring procedures such as Fuzzy
Knowledge Bases are appropriate for seeking “the best” site. However, they are not
designed to evaluate bet “sets,” which requires the simultaneous evaluation of different
combinations of sites. We describe an approach towards solving this kind of problem in
the next section.
6
4
Identifying a representative set of nature reserves using GIS and
simulated annealing
4.1
Reserve Ssystem Design
The ongoing mass extinction of terrestrial and marine species is a direct result of human
activities that result in habitat fragmentation, spread of exotic species, overharvest of
species, environmental pollution, changes in disturbance regimes and biogeochemical
cycles, and climate change (Vitousek et al., 1997; Novacek and Cleland, 2001). Although
we do not understand the full consequences of losing perhaps 15-30% of the word’s
species over the next 50 years, we do know that extinction is not occurring uniformly.
Species are not distributed uniformly and neither are human impacts. Conservation
planners seeking to locate new nature reserves to reduce the risk of species’ extinctions
are now taking advantage of existing information on the distributions of species and
threatening human activities to try to prioritize the best places to focus conservation
investments. At global and continental level, planners have proposed focusing
conservation on threatened hotspots of biodiversity that support high species richness
and endemism ((Olson and Dinerstein, 1998; Myers, Mittermeier et al., 2000; Pimm and
Raven, 2000). At the national to regional level, conservation planners have emphasized
the need to establish representative systems of reserves that capture the full range of
species and environments (Scott, et al.,1993). At the sub-regional and local levels,
conservation planning is increasingly focused on saving core habitat areas for species and
corridors connecting those core areas (Forman and Collinge, 1997 ). Issues of reserve
design and the costs of land conservation, restoration and management enter into
planning criteria at this level. Prioritization often is heavily influenced by knowledge of
current threats, costs, and opportunities.
Much of the research on systematic conservation planning at the regional scale has
focused on methods to help planners identify a set of sites that would collectively
represent the biodiversity of the region (Pressey, Humphries et al. 1993; Margules and
Pressey 2000). Knowing the “best” set of sites for achieving this goal is helpful in
evaluating areas that are being considered for conservation as well as highlighting new
areas that merit attention.
It is usually extremely difficult to identify an optimal reserve system that would represent
all targeted species and communities (conservation “elements”) because the number of
conservation elements and planning units is large (typically hundreds of elements and
hundreds-to-thousands of planning units), making the number of possible portfolios far
too large to search exhaustively for the set of sites that best meets the stated conservation
goals. It is a “wicked” problem because we have very incomplete data and information
about species and community distributions, threats and costs.
Over the past fifteen years conservation planners have developed computer-based
approaches to make the site selection process more systematic and more explicit
(Church, Stoms et al. 1996). These approaches respond to the perceived need for reserve
siting to be as efficient or cost-effective as possible, given the competing social and
economic demands for land and water. They also address the concern that reserve system
design should be repeatable, so that the reserve systems can be readily re-evaluated and
7
modified over time as conditions change and new information is acquired. These
approaches assist planners in sorting through the large volume of data to identify good
initial solutions. A planning team must still review the initial solutions and modify them
using local knowledge, judgment, and other evidence not considered in the reserve
selection approach.
4.2
The Sites Model
Recently with funding from The Nature Conservancy we produced GIS-based spatial
decision support software named Sites to help conservation planners identify
representative reserve systems in a region. The software combines GIS visualization
capabilities of ArcView (ESRI ™) with a simulated annealing selection algorithm called
SPEXAN (Spatially Explicit Annealing) that was developed by Ian Ball and Hugh
Possingham in Australia. Unlike previous reserve selection algorithms, SPEXAN
provides the ability to consider both site cost and spatial configuration as well as site
biological composition in choosing the best set of planning units from a planning region.
The Sites and Spexan models are summarized here. Model software, a user’s manual,
and more detailed description and data for the case study described below can be
downloaded at http://www.biogeog.ucsb.edu/projects/tnc/toolbox.html.
The overall objective of the portfolio selection process is to minimize the cost of the
portfolio while ensuring that all conservation goals have been met. The conservation
goals include representation goals and goals for spatial configuration. Representation
goals could be the specified number of population occurrences of a species or total area
of a plant community that must be included in the regional portfolio. Spatial
configuration goals specify either a minimum distance by which sites must be separated,
or conversely the relative importance of selected sites being contiguous to achieve spatial
compactness and connectivity of the final portfolio. A spatial search model is used to
minimize the objective cost function:
Total Portfolio Cost = (cost of selected sites) + (penalty cost for not meeting the stated
conservation goals for each element) + (cost of spatial dispersion of the selected sites as
measured by the total boundary length of the portfolio). More formally:
Total Cost   Cost site i 
i
 Penalty cos t
for element j  wb  boundary length
j
(Eq. 1)
The algorithm seeks to minimize Total Cost by selecting that set of sites which covers as
many elements as possible as cheaply as possible in as compact a set of sites as possible.
The actual solutions depend on how site cost is measured, on the target levels and the
penalty cost for each element (these are set separately for each element), and on how
heavily one weights boundary length (using the boundary modifier, wb) as an additional
cost factor. One of the tricky parts of using Sites effectively is getting site, element and
boundary costs onto a comparable scale, whether it be area, financial cost, or some
arbitrary value.
Spatial complexity can be included in any of three ways: 1) the boundary length of the
portfolio can be included in the objective function and valuation of the portfolio to
8
encourage clustering; 2) given conservation elements can have an aggregation rule applied
to them to eliminate small fragments; and 3) given conservation elements can have a
separation rule applied to them to protect against local catastrophes.
We solve for the collection of sites that minimize total cost using a heuristic search
procedure known as simulated annealing (Ingber 1993). The solution method that begins
with an initial set of sites and iteratively seeks to improve on that set. At each iteration,
sites are swapped in and out of that set and the change in cost is measured. If the change
improves the set, the new set is carried forward to the next iteration. However, even
changes that increase the cost (that is reduce the quality) of the set may be carried
forward, so that one can examine a greater number of different site combinations. The
changes to the selected set can be large at first (even sites that contribute greatly to
reducing cost can be removed) but then allowable changes are made progressively smaller
as the total cost of the solution diminishes. The method’s name is based on an analogy to
the metallurgical process of heating followed by slow cooling to toughen a material.
Simulated annealing has been applied to a number of optimization problems and
generally provides better solutions than simpler, stepwise iterative methods (Possingham,
Ball et al. 2000). The method is computationally demanding, the results are sensitive to
annealing parameters (which are generally set through trial and error), and the procedure
is not guaranteed to find an optimal solution. Performance improves with more
iterations. We typically use a minimum of 1,000,000 iterations, which requires a few
minutes for a moderately large problem. We also repeat the procedure 10 or more times
and then choose the best solution from the set of runs. Sites provides a simple graphical
interface that lets the user readily select the number of iterations, number of model runs,
and also offers a variety of different options for model output.
4.3
An Example of reserve system design in the Sierra Nevada of
California
For the past decade the Sierra Nevada mountain range in California has been the focus
of a great deal of conservation analysis on both public and private lands (Figure 4). The
region, which extends over 600 km from north to south, is famous for its dramatic
scenery including places like Lake Tahoe, Sequoia and Yosemite National Parks. The
massif of the southern Sierra Nevada includes Mt. Whitney, which at 4,418 m is the
highest peak in the United States outside of Alaska. The area is also rich in plant and
animal species, many of them endemic to California. For example, roughly 3,500 plant
species occur in the region, more than half of California’s native plant species Over 400
vertebrate species occur in the region.
As is the case in most of the western United States, public lands in the Sierra Nevada are
mainly located at middle and high elevations which support conifer forests, subalpine
woodlands and alpine meadows. The foothills of the range are almost entirely privately
owned and are dominated by grasslands, oak woodlands and chaparral. Historically these
lands were used mainly for livestock grazing but are now undergoing rapid development,
especially in the central region near major metropolitan areas such as San Francisco and
Sacramento. The foothill zone supports a rich variety of plant communities and wildlife
species, although biodiversity has been heavily impacted by exotic species, altered fire
9
regimes, and habitat fragmentation, especially the aquatic and riparian components of the
system. Many public and private organizations are now focusing on creating new nature
reserves in the foothill zone to protect these natural resources before they become
severely fragmented and degraded.
The Nature Conservancy has initiated a new program of conservation planning with the
aim of protecting viable examples of all plant communities in every ecoregion of the
United States. We demonstrated the use of the Sites model for The Nature Conservancy
to generate conservation portfolios for a 28,000 km2 area of the northern Sierra Nevada.
TNC analysts established community-specific representation goals that varied depending
on the nature of the community. For example, the goal for widely distributed
communities was to protect 30% of the current distribution of each community in the
region. The goal for a uncommon and highly restricted communities was as high as 70%
of current distribution.
We used vegetation community data are from the California Gap Analysis Project.
Seventy-nine vegetation types were mapped, although several of these are urban,
agricultural, or otherwise not of conservation interest. For planning units we selected
776 small watersheds (mean size of approximately 3,500 hectares). Data developed for
for each planning unit included the current ownership and management status of the
unit, the area of each vegetation type, the "cost" of the unit), and the length of shared
boundary between adjacent planning units. Because we did not know the actual cost of
protecting a planning unit, we estimated relative costs as a weighted sum of the unit’s
area and a suitability factor calculated as a weighted sum of percent of area in private
ownership, complexity of public and private land ownership pattern, human population
density, and extent of unit affected by roads (Figure 5, see Davis et al 1996 for details).
Data for each vegetation type include the name of the type, the representation goal, and
optional flags affecting the weight of the type in meeting representation goals and the
spatial behavior of the model.
Figure 6 shows the regional distribution of one particular plant community with the Sites
solution superimposed. As expected, the actual model solution varies depending on the
boundary weight modifier (Figure 7). As the boundary weight (wb in Equation 1) is
increased from 0 to 0.2 to 1.0, the watersheds in the solution becomes increasingly
clumped and the area required to meet the representation goals for each type increases.
Using Sites it is also easy to examine how the reserve solution varies as a function the
representation goals and by how one defines the starting reserve system. The software
allows the user to lock specific planning units into or out of the solution. Also the user
can view the distribution of each conservation element compared to the reserve system
selected by the model.
SPEXAN uses some randomization techniques to work towards good solutions. This
means that every model run may be relatively different just by chance. When the model is
run many times, it is also useful to examine how many times each planning unit appears
in a solution. This provides some indication of the “irreplaceability” of each planning
unit as well as the overall robustness of the best result. Tabular outputs include more
detailed information on each model run, including statistics about the overall cost and
10
composition of the reserve network and how much of each community type is protected
relative to the initial conservation goals.
4.4
Strengths and Limitations of the Approach
The Sites model and other reserve selection models have proven extremely useful for
regional conservation planning. Such models provide a conceptual framework for
organizing and analyzing both biological and socioeconomic information. They require
that explicit, quantitative conservation goals be set for the region. In our experience the
planners gain a lot of insight about the distribution of resources and conservation
opportunities simply by exploring alternative assumptions and model parameter values.
The ability to quickly and interactively develop and visualize alternative portfolios allows
different parties to examine competing goals and weights, a process that has been shown
useful in facilitating negotiation between different interest groups in a region.
As in the previous example, the value of the output from Sites depends on having
reliable information on biological and social factors. The case study described here lacked
a formal accounting of potential bias and uncertainties caused by uneven data quality and
expert knowledge. Potential changes in land use and environmental factors such as
regional climate were not considered explicitly. More generally, we did not attempt to test
the viability of any of the target elements at any of the planning units or over the
collection of planning units, which would have required scenarios of future habitat
conditions for both protected and unprotected sites in the region. Other systematic
approaches also suffer from many or all of these deficiencies, and issues such as
uncertainty, optimal staging of implementation, and improving portfolio design for
viability are all areas of active research. In the last section we describe an ongoing effort
to incorporate more formally economically-based scenarios of land use change and the
viability of species in alternative reserve systems.
5
Summary
Conservation planning to site new reserves has evolved from an unstructured process to
include more systematic procedures for using information on species, habitat factors,
socioeconomic factors, and other criteria. Multicriteria scoring procedures have proven
effective for identifying single conservation sites, whereas set selection procedures are
needed to identify sets of sites that collectively satisfy conservation goals.
Neither of the approaches illustrated here formally addresses the question of the viability
of species in reserve systems. Research is now focused on ways to more formally
consider reserve design issues such as the size, shape, and context of the protected area
and to evaluate reserve systems using population viability analysis and metapopulation
models. Thus far these dynamic ecological models have not been incorporated formally
into the reserve selection procedures described here, except in a post hoc fashion. Another
important research direction is to develop approaches that more explicitly consider
alternative economic policies and their effects on optimal reserve systems.
11
6
Acknowledgments
The research described here was supported by the University of California Office of the
President, and The Nature Conservancy. We are grateful for the support of these
sponsors.
7
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14
Table 1. Summary of planning regions and planning units in the 3-stage reserve siting
process for the University of California at Merced.
Stage 1
Planning Region
Planning Units
Criteria
63,000 km2 are
including San Joaquin
Valley and Southern
Sierra Nevada
Mountains
1400 Watersheds and
townships;
Good ecological
condition
mean area 4,500 ha.
Number of rare and
endangered species
and communities
Near UC Merced
campus
Complementary to
existing UC reserves
Stage 2
12,628 km2 region
surrounding the
Marced campus site
623 units defined by
roads, ownership and
drainage boundaries;
mean area 2,027 ha
High density of vernal
pools
Good ecological
condition
Feasible for
acquisition and
management
Close proximity to
UC Merced campus
Stage 3
430 km2 area
including the
proposed UC Merced
campus site and
neighboring areas.
298 Tax Assessor’s
Parcels;
mean area 144 ha.
High local ecological
diversity
Large, intact area of
vernal pool and
grassland habitat
Good watershed
integrity
Favorable acquisition
and management
traits
15
8
Figure Captions
Figure 1. Location map of the study area for the UC Natural Reserve System study, and
the assessment regions of the three stages.
Figure 2. The network for the Stage 2 proposition that the “site highly suitable for an
NRS vernal pool reserve.” Networks are shown as ovals and data links are rectangles.
Figure 3. Map of truth values for vernal pool site suitability for the stage 2 assessment
region. The bold black outline with white inner line shows the Stage 3 planning
boundary.
Figure 4. The Sierra Nevada region of California.
Figure 5. Map of suitability of planning units in the northern Sierra Nevada used along
with planning unit area to estimate conservation cost of each planning unit. Darker units
have lower suitability (higher cost).
Figure 6. Map showing a Sites solution with moderate spatial clumping (boundary layer
modifier of 0.2) overlaid on map of planning units containing Foothill Pine-Oak
Woodland. This shows the distribution of where this type would be protected in this
particular alternative.
Figure 7. Comparison of reserve system alternatives produced using Sites with varying
boundary length modifiers (BLM). Leftmost map has a BLM of 0.0, or no clumping
enforced. The center and right maps have increasing BLM values that enforce greater
clumping.
16
Figure 1.
17
Stage 2
Site is highly
suitable for NRS
vernal pool reserve
AND
Vernal pool
habitat is
suitable
Scientifically
suitable
Administratively
suitable
Academically
suitable
Vernal
pool
density is
high
Ecosystem
has integrity
AND
Accessible
for field
trips
OR
Easy to
acquire/manage
Little native
habitat has
been lost
AND
Road effect
zone is
small
Acquisition
terms favorable
Representation
increased
Important
for
SJESRP
Easy to
administer/
maintain
OR
Few
owners
involved
Figure 2.
18
Large
parcel
exists
Risk of
development
is low
Figure 3.
19
Figure 4.
20
Figure 5.
21
Figure 6.
22
Figure 7.
23