**A GUIDE TO IMPLEMENTING THE TECHNICAL ELEMENTS OF STRATEGIC HABITAT
CONSERVATION: VERSION 1.0**

**A report from the Strategic Habitat Conservation Executive Oversight
Council and National Technical Advisory Team**

**October 10, 2007**

**Introduction**

This report describes a recommended framework for strategic habitat
conservation (SHC) enabling the efficient conservation of wildlife
populations through [habitat management]{.underline}, which we define as
[protection of existing habitat, and habitat restoration or
manipulation]{.underline}. It supplements the description of the SHC
framework in the final report of the National Ecological Assessment Team
(NEAT), accepted by the Service Directorate in July, 2006. While the
NEAT report was developed specifically for the Service Directorate and
USGS Executive Leadership Team, we believe it was useful to all Service
and USGS employees in conveying the basic elements of SHC. This report
provides much more detail on applying the technical elements of SHC;
biological planning, conservation design, outcome-based monitoring and
assumption-driven research. As such, it was written for two primary
audiences: 1) technical staff who will be performing these elements and
need to understand how they should be developed and implemented; and 2)
Assistant Regional Director, Program Managers and Project Leaders who
need to be able to distinguish conservation planning tools based on the
SHC framework from those founded on a different approach.

The essence of SHC is setting explicit objectives for populations and
then figuring out how to attain these objectives most efficiently using
our own resources and by working with partners. Although the focus of
SHC is on species that are limited by habitat, and for which a habitat
management solution exists, *strategic* is operative word in the phrase.
Implementing the full SHC framework will make the Service more
efficient, transparent, and accountable; and ultimately more credible
and effective in informing the actions of policy makers and other
agencies. While the focus of SHC is obviously on the conservation of
populations limited by habitat, the two most fundamental features of SHC
-- 1) establishing explicit, measurable objectives and 2) the use of
models relating populations to limiting factors to target management and
assess its impacts -- are applicable to other functions of the Service,
e.g., regulating take and developing law enforcement strategies.

**Background**

The conventional model of habitat conservation by fish and wildlife
agencies has for many species, at best, slowed the rate of population
decline. This is largely due to insufficient management resources
compared to ever increasing human pressures on natural systems and to
insufficient regulatory authorities. We believe that both are primarily
due to three factors: lack of explicit and socially-accepted
conservation objectives, lack of clear compelling conservation
strategies that describe why populations have declined and what may be
done toward their recovery; and a limited ability to demonstrate the
population effects of our management actions. Collectively, these
factors contribute to a lack of awareness by the public, elected
officials, and representatives of other government agencies that reduces
the credibility and influence of wildlife management agencies.

The traditional approach to conservation in many areas can be
characterized as an agency operating with limited awareness of the goals
and the potentially beneficial or adverse activities of other agencies
working in the same landscapes. Planning is often viewed as onerous and
the plans themselves as static documents with limited value. Research
and monitoring may be perceived to be expensive luxuries with little
relevance to making management decisions.

Conversely, the approach we recommend in this report is predicated on
inter-agency collaboration and coordination, is planning intense and
requires the integration of planning, management, monitoring and
research. Although we will not discuss it in detail, the concept of a
conservation business model is gaining acceptance (Keen and Oureshi
2006). Successful businesses must articulate their purpose, develop
products, identify target markets and marketing strategies, and create
feedback loops that insure product quality and continued viability in a
competitive environment (Prahalad and Hamel 1990, Drucker 1994, Keen and
Qureshi 2006). These concepts are developed in a series of communication
and marketing tools designed to inspire investor confidence. It is
convenient to refer to this collection of tools as a business plan. A
conservation strategy serves the same purposes.

The idea of inspiring investor confidence may initially sound like an
odd concept for government agencies; however, the competition for public
funding may be as intense as competition in the marketplace. Inspiring
investor confidence requires that agencies demonstrate their ability to
efficiently achieve results. This may be the greatest failing of the
traditional conservation paradigm. Even small budget increases carry an
implicit expectation that perceptible benefits will result. Failure to
produce these perceptible benefits reduces investor confidence. Although
the general magnitude of the challenge of conserving populations at
objective levels may be intuitive to conservation professionals, most of
the public and elected officials are lay people that routinely lack this
understanding.

To be more successful, it is imperative that wildlife management
agencies be more explicit about objectives, strategies and estimated
costs of attaining objectives in order to build increased support by the
public. Furthermore, we believe that wildlife management agencies must
more fully adopt the role of stewards and purveyors of the biological
foundation for conservation, seeking to inform and influence the actions
of other government agencies and policy makers. We believe that
developing and communicating explicit, science-based conservation
strategies are critical to building this support and that the concepts
we present in this report can help remedy current deficiencies. The
framework we describe places the use of models in a useful context of
the larger conservation enterprise. It is based on our personal
experiences in attempting to meet the information needs of managers in
government wildlife management agencies, and it is not a synthesis of
the extensive literature on theories of conservation biology.

**AN OVERVIEW OF STRATEGIC HABITAT CONSERVATION**

We define strategic habitat conservation (SHC) as an iterative process
of developing and refining a conservation strategy, making efficient
management decisions, and using research and monitoring to assess
accomplishments and inform future iterations of the conservation
strategy (Fig. 1). SHC is simply a specific form of adaptive resource
management (Walters 1986, Walters and Holling 1990, Williams 2003)
wherein habitat management is the primary form of intervention.

The goal of SHC is to make natural resource management agencies more
efficient and transparent, and, in part, thereby making them more
credible and wide-reaching in effect (Johnson et al., at press).
*Conservation efficiency may be thought of as the ratio of population
impacts to management costs*. Science-based conservation strategies are
developed to increase efficiency over that attainable with more random
or haphazard conservation delivery. This presumes two facts: (1) that
sites vary in their potential to affect populations in a predictable
fashion; and (2) that habitat managers are able and willing to
prioritize their actions, i.e., operate strategically.

A comprehensive conservation strategy must address five basic questions:

1.  Why have long-term average populations declined?

2.  What do we want to achieve and how can we achieve it?

    a.  What are our objectives for populations?

    b.  What factors are acutely limiting populations below objective
        levels?

    c.  What management treatments are available to overcome these
        limiting factors?

3.  Where should we apply these management treatments to effect the
    greatest change in populations at the lowest possible total monetary
    and non-monetary costs to management agencies and societies?

4.  How much of a particular type of management will be necessary to
    reach our population objectives (a habitat objective -- a minimum
    estimate, but useful nonetheless for reasons we will describe).

5.  What are the key uncertainties in the answers to questions 1-4 and
    what assumptions were made in developing the strategy? These will
    guide our research and monitoring activities.

In the case of federal and state fish and wildlife management agencies
it is usually most appropriate to ask and answer these questions in
terms of populations; however, these basic questions are equally
applicable to other ecosystem functions. Other agencies and
organizations with different mandates may focus on these other functions
by applying the same basic concepts. In this report we will discuss a
framework for SHC for the conservation of populations limited by loss or
deterioration of habitat.

Efficient conservation requires that agencies strategically apportion
their resources at broad scales. This commonly means that agencies must
undertake SHC in multiple regions since the relationship of a species to
its habitats is likely to vary in space. SHC will be more efficient when
it is implemented in geographic regions (i.e., ecoregions) for which
species of concern, population-habitat relationships, including limiting
factors, and possible future threats to habitats, are relatively
homogeneous. This enables the use of strategies tailored to a particular
part of a species' range and to a particular season of the year, if
necessary, and it also enables more reliable inferences from research
and monitoring. Conducting SHC within ecologically-based regions such as
Bird Conservation Regions (U.S. Fish and Wildlife Service 1999, Sauer et
al. 2003) is a logical way to organize strategic conservation across a
country or continent.

**SHC TECHNICAL ELEMENTS**

Our framework for SHC consists of an iterative cycle of five mutually
supporting elements that exist in two broad realms -- a technical realm
consisting of [biological planning]{.underline}, [conservation
design]{.underline}, [assumption-driven research,]{.underline} and
[mission-based monitoring]{.underline} and a management realm consisting
of the suite of management and administrative functions that comprise
[conservation delivery]{.underline} (Fig. 1). In this report we will
focus on the technical realm of SHC.

The framework we describe in the remainder of this report is not
proposed as a rigid, linear sequence of events (Fig. 2). Distinctions
between biological planning, conservation design, conservation delivery
and research and monitoring are somewhat artificial, with each element
blending into the others in an iterative process. However, the process
achieves its full value only when all five elements are in place.

**Biological Planning**

Biological planning is the systematic application of scientific
knowledge about species and habitat management. This means that we
articulate measurable population objectives for selected species,
consider what may be limiting populations to less than objective levels,
and compile models that describe how populations are expected to respond
to habitat management.

*Select Focal Species. \--* The list of priority species for an
ecoregion provides a starting point to select a smaller subset of focal
species to use in SHC. Ideally we would model species-habitat
relationships and spatial patterns in management potential for every
priority species; however, the use of focal species is usually a
necessary planning and design "shortcut." Moreover, trying to integrate
information about too many species representing key ecological processes
can become overwhelming.

Focal species are used to represent the needs of larger guilds of
species that use habitats and respond to management similarly; however,
focal species may be more sensitive to patch characteristics, landscape
context, or habitat management (Lambeck 1997, 2002). Other focal species
may have unique habitat needs (e.g., some T&E species) or may be
keystone species and therefore important determinants of ecosystem
function (Mills 2007). Hagan and Whitman (2006) provide a valuable
overview of the use of indicator species. They recommend selecting 5-15
species that are sensible indicators of the ecological communities and
processes stakeholders value most. Of course, the assumption that other
species and ecological processes will respond as predicted to habitat
protection, restoration, and management must be evaluated (Lambeck
2002).

We believe that the use of multiple focal species will typically be more
satisfactory than the use of a single umbrella species (Lambeck 1997,
2002, Lindenmeyer et al. 2002). There is no single prescription for
selecting focal species or the number of focal species (Hagan and
Whitman 2006, Mills 2007). Focal species may be selected for biological,
socio-economic, programmatic, or political reasons. One useful method
for selecting focal species may be to start by assigning species to
guilds based on their basic habitat needs and response to management.
One or more focal species may be selected from each guild (Example A).
Because one outcome of SHC is an objective for each general habitat
type, it will often be important to also select focal species with large
enough population objectives to insure adequate habitat to meet public
demand for these species. Often these will be high profile game species
that are actually less limited in their habitat use than some other
species. The USFWS may be better served by selecting focal species that
help us make better decisions about managing our trust resource
responsibilities. Likewise, partners may select the focal species that
best meet their needs. This does not preclude a continuous dialog with
partners, but agencies with different trust responsibilities plan
separately for focal species and [then]{.underline} integrate the
outcomes of the biological planning processes.

*Set Population Objectives. \--* Efficient conservation strategies can
be developed only after unambiguous mission-based objectives are
established. Unlike some past approaches to conservation which have
tended to view activities like wetland restoration or reforestation as
objectives, SHC requires explicit objectives for populations because
most agencies are charged with the conservation of populations -- not
habitat.

If our mandate ***was*** simply to conserve habitats an objective like
["Restore wetlands in the Great Lakes ecoregion of the US"]{.underline}
might be adequate. However, an activity--based objective like this does
not promote accountability because no explicit relationship has been
established between habitat accomplishments and the mandate to conserve
populations. It is an objective without a clear ending point and without
benchmarks for success, i.e., the objective is to do more wetland
restoration each year. Of significant concern, a habitat objective
without a clearly articulated set of predicted population outcomes
provides no justification for increased resources for conservation,
because there are no tangible predicted consequences to populations or
the public of success or failure.

SHC is founded on objectives expressed as desired population states,
such as ["Maintain an average annual capacity to produce 1.7 million
duck recruits/year in the Great Lakes ecoregion of the US."]{.underline}
These are called "mission-based objectives." Efficient attainment of a
mission-based objective requires that we know the current state of the
system relative to the objective, make informed assumptions about
environmental factors that are limiting populations below objective
levels, and determine where and how management can most effectively
remediate these limiting factors. Furthermore, we acknowledge that site
and landscape-scale factors interact to affect the population impacts of
management. Thus, *where* we deliver management is an important
determinant of *how much* habitat is required to sustain populations at
objective levels. These are the basic elements of a conservation
strategy and efficient conservation delivery.

Population objectives may be more useful if they are comprised of
desired abundance and a performance indicator. For convenience, we will
refer to these as P~1~ and P~2~ subobjectives, respectively. Examples of
hypothetical population objectives might be:

1.  Maintain a population of 1,250 moose (*Alces alces*) (P~1~) in
    northwestern Minnesota with a mean annual calf:cow ratio of 0.84
    (P~2~);

2.  Increase king rail (*Rallus elegans*) density 300% (P~1~) at marsh
    bird survey sites and maintain a mean annual nesting success of 60%
    (P~2~) in the southeastern coastal plain; or

3.  Maintain 25 distinct stream segments (P~1~) with stable or
    increasing (P~2~) breeding populations of lake sturgeon (*Acipenser
    fulvescens*) in Michigan.

In each case, the P~1~ subobjective enables us to estimate how much
habitat we need to maintain based on our limited knowledge of relative
habitat suitability, territory size, population viability, probability
of occupancy or average density in suitable habitat. Above minimum
viable population sizes, P~1~ subobjectives are value-based expressions
of how many individuals of a species we want, or, more accurately, that
we believe the public wants and will support. Ecoregional-scale P~1~
objectives should be stepped down from range-wide objectives when these
broad-scale goals exist; doing so links local conservation actions to
national or continental strategies and vice versa.

P~2~ subobjectives, which are commonly vital rates, describe how we want
to affect the population. If we believe that some habitats yield higher
productivity or density than others, the P~2~ subobjective should help
us decide how to configure or manage the habitats we conserve. In
practice, it will often be necessary to express P~2~ subobjectives as
assumptions about the effects of management.

Although vital rates are notoriously difficult to estimate, monitoring
both P~1~ and P~2~ subobjectives paints a much clearer picture of how we
are influencing focal species populations and ecological function than
we would get from monitoring abundance alone because estimating
short-term trends from annual abundance data often requires unattainably
intensive monitoring. For some species, P~1~ and P~2~ subobjectives may
be combined, as in the Great Lakes duck example above, in terms of
number of recruits produced, rather than a P~1~ subobjective for number
of a breeding pairs and a P~2~ subobjective for recruitment rate.

*Identify Limiting Factors and Appropriate Management Treatments. --*
The purpose of habitat management is to relieve the constraints limiting
factors impose on population size. *"The presence and success of an
organism or group of organisms depends upon a complex of conditions. Any
condition which approaches or exceeds the limits of tolerance is said to
be a limiting condition or a limiting factor...first and primary
attention should be given to factors that are operationally significant
to the organism at some time during its life cycle"* (Odum 1971). One
purpose of SHC is to identify areas where these limiting factors can be
most efficiently alleviated, i.e., areas where:

-   potential population impacts are relatively high;

-   management costs are relatively low; and

-   tactics are socially acceptable.

Limiting factors are often related to the appropriate area, type,
quality, or configuration of habitat necessary to sustain a population
at objective levels. For example, consider a hypothetical example in
which low reproductive success in small forests limits populations of a
species of interior forest breeding bird. There are not enough large
patches to sustain the population at objective levels of abundance.
Individuals that settle in small patches fail to recruit young into the
population, so the population must be maintained by birds that are able
to settle in large patches. Once we understand the limiting factor,
several potential management treatments designed to increase recruitment
or survival may be considered:

1.  Use reforestation to create more large patches

2.  Focus on increasing non-breeding survival

3.  Use nest predator and nest parasite control in small patches

4.  Raise birds in a hatchery and release them into the wild

Generally, one or two management treatments will be most practical and
compatible with our goals for the ecosystem and the other species that
inhabit it. In this case, managers would likely choose reforestation as
the preferred management treatment -- coalescing small patches where
recruitment is low into larger patches where recruitment is higher. If
survival remains the same and reproductive success increases in response
to increasing patch size, the population will grow toward objective
levels. Hence, a primary purpose of the conservation strategy for the
guild of interior forest breeding birds in this ecoregion would be
strategic targeting of reforestation to most efficiently increase the
number or area of large patches.

*Develop and Apply Models. \--* Developing an efficient conservation
strategy requires that we understand the relationship between
populations and limiting factors. A defining feature of SHC is the
application of models to spatial data to target specific management
treatments. Models are simply a means of organizing our science to aid
in understanding how a system functions by expressing real relationships
in simplified terms (Starfield and Bleloch 1991).

Whether aware of it or not, almost all managers use models to predict
the probable outcomes of applying a particular management practice in a
particular site in its landscape context. The difference between this
intuitive approach to modeling and the more deliberate use of models in
SHC is that, in the latter, models are stated in explicit, measurable
terms. The advantages of explicitly stating and systematically applying
models are that:

1.  Models and the products of applying them are useful for
    communicating the scientific foundation for actions, decisions, and
    recommendations, thereby yielding greater transparency and
    credibility;

2.  The process of explicitly stating a model enables us to critically
    evaluate our uncertainties and assumptions and thus:

    a.  Determine how confident we should be about our predictions; and

    b.  Target critical information needs to make future predictions
        more reliable.

3.  Explicit models may be used to report accomplishments expressed as
    estimated population effects.

Although it is tempting to focus on using models to make maps of
conservation priority areas, these other benefits of using models are
often just as important.

For our hypothetical focal species of forest breeding bird example, we
believe that it is really the ratio of patch edge:area that limits
recruitment rate -- as patches become larger and blockier, recruitment
rate goes up. Thus answering the questions of *where* and *how much*
requires that we use models that describe the relationship between the
ratio of perimeter:area and recruitment rate (Fig. 3). In this example,
we see that after the perimeter:area ratio exceeds 0.1 (about 125 acres
for a square patch, larger for irregularly shaped patches), further
increases in recruitment rate begin to slow down. We have reached the
point of diminishing returns. A strategic approach to attaining our
objectives, as informed by this model, would indicate that once we have
reached a ratio of 0.1, we should move on to a new patch rather than
continue to make the same patch bigger and bigger for less and less
additional benefit.

The value of a model is measured by the extent to which it adds useful
information to the management of focal species. Generally speaking, as
model complexity goes up, so does the added value for decision making
because model predictions move beyond our capacity for intuition.
Advances over the last two decades in spatial data management enable
depiction of complex multi-dimensional biological models in
two-dimensional (map) form that contribute to a better understanding of
how management potential varies among landscapes. However, models and
the maps derived by applying them to spatial data have inherent
uncertainties that should be explicitly acknowledged but seldom are.
Burgman et al. (2005) recommend using multiple competing models in
pursuit of robust management strategies that are likely to result in
tolerable outcomes, despite uncertainties.

Numerous types of models are described in the literature. We describe
the most basic dichotomy among types of models as data-based (empirical)
and experience-based (conceptual) models. Niemuth et al. (at press)
present empirical models for breeding duck access to grasslands, sora
(*Porzana carolina*) use of wetlands, and empirical and experience-based
models for marbled godwits (*Limosa fedoa*) (Examples B, C and D).

Both empirical and experienced-based models may be used to predict
factors (in increasing sophistication) such as probability of occurrence
or apparent habitat suitability, abundance or density, and demographic
rates such as productivity or survival (Fig. 4). Each may be estimated
in relative or absolute terms. Generally, models tend to be more
data-driven and less experience-based as the sophistication of their
predictions increases. For example, although experienced-based modeling
like that for marbled godwits in Example B is useful for predicting
relative apparent habitat suitability, the outcome of estimating
abundance using a purely experienced-based approach would be less
certain. However, if apparent habitat suitability is all we are able to
reliably predict, we may still predict abundance using
empirically-derived average density estimates from "suitable" versus
"unsuitable" or "less suitable" sites.

Estimating the effects of habitat management on population vital rates
is an ideal that is presently impossible for most species because
appropriate data for model development do not exist. However, estimating
probability of occurrence or even relative abundance is possible for
nearly every species although these models may contain numerous
initially untested assumptions.

In strategic habitat conservation, model predictions must be expressed
in the same terms as population objectives to (1) estimate the amount of
habitat management necessary to attain population objectives; and (2) to
facilitate estimates of project, program or agency accomplishments and
net progress toward population objectives. The implications are that the
information available to create models will affect the form of model
predictions, which in turn affect the expression of population
objectives. Thus, data collection, model development and population
objectives are iterative within the overall cycle of SHC.

**Conservation Design**

Conservation Design is predicated on the belief that the potential to
affect populations varies in space in response to site characteristics
and landscape context. If not, it matters little where we conserve
habitat. The development of maps predicting patterns in the ecosystem is
the outstanding feature of Conservation Design. Maps that are not based
on the systematic application of science can be misleading and may
impede conservation success. Maps used in SHC are the product of
applying empirical or experience-based models to spatial data. Hence we
propose the use of the phrase "spatially-explicit models" (SEM) in lieu
of maps to emphasize that developing and applying models relating a
species to limiting habitat factors is the essence of SHC.

***Assess the current state of the ecosystem --*** A conservation
strategy is a route between where we are (the current state of the
system) and where we want to be (the objective state). Models used to
create SEMs also may be used to estimate the current state of the
system. The current state of the system must be expressed in the same
units as population objectives. The objective state minus the current
state represents a conservation deficit to be made up as efficiently as
possible. Note that the deficit is expressed in terms of populations,
not acres of habitat.

***Develop species-specific SEMs. --*** SEMs will generally be specific
to a focal species and a management treatment that affects that species,
e.g., targeting a particular management treatment like reforestation or
wetland protection to address factors limiting populations below
objective levels. For example, the ornate box turtle (*Terrapene
ornata*) is listed as threatened or endangered in a number of Midwestern
states. Population declines are primarily attributable to the loss of
sandy-soil grasslands and road-related mortality within remnant
populations. Management treatments, therefore, include (1) strategic
grassland restoration on sandy sites (away from roads) and (2) road
signage placed around known or suspected populations. In this situation,
an SEM for ornate box turtles may be based on a simple empirical or
experience-based model with only two variables -- land cover and soil
type. SEMs derived from these models may be combined with data on the
distribution of roads to identify areas with existing or potential
turtle populations for population surveys, potential population
restoration sites, and sites to erect road signage.

***SEMs typically include an assessment of the potential of every part
of the ecoregion to impact a population or set of populations. This
means that geographic units with high, moderate and even low potential
to affect populations are included. This is important because 1)
managers typically only deal with willing landowners and it is not
always possible to limit management to the highest priority sites; and
2) a management action with a lower predicted biological impact may
still be efficient if management costs are low enough.***

The resolution of SEMs should match or be smaller than the scale at
which management occurs. Maps of large geographic units like counties or
major watersheds may be deceptive because they implicitly include the
unreasonable assumption that management anywhere within the county or
watershed will yield the same outcomes, i.e., they are simply too coarse
to reflect site and landscape effects on potential management outcomes.
The geographic units assessed using models and portrayed in SEMs should
be small parcels that more or less match, or are finer than the typical
scale of management (i.e., as fine as possible but generally \<640
acres) (Fig. 5).

***Formulate habitat objectives \--*** Habitat objectives are developed
for habitat types, not species. The size of an objective for a
particular habitat type depends on the diversity of species that depend
on it, their population objectives, and on their range of responses to
management. For example, grassland habitat objectives for an ecoregion
will be smaller if every priority species prefers idled grasslands, than
if some prefer idled and some disturbed habitat, because the potential
for aggregate population impacts is greater for each acre.

Habitat objectives may be expressed for the total area of habitat in
public and private, protected and unsecured status, or they may be
defined more specifically, such as the number of acres to be restored
and placed in the conservation estate. In theory, if we know the
capacity of every acre to contribute toward our population objective for
each focal species, we can simply tally up the smallest area (cost/acre
being equal) that overcomes the aggregate conservation deficit. Of
course, this is an absolute minimum estimate of the amount of habitat
that will actually be required to achieve population objectives since it
is almost never possible to work exclusively in the areas with the
greatest potential to affect populations.

Since potential to affect populations varies in response to site
characteristics and landscape context, the relative efficiency with
which we make up the conservation deficit, and attain our population
objectives, depends on our ability to act strategically by operating at
sites with the greatest potential to affect each focal species'
populations and reconcile potential management conflicts. Because we can
not typically work exclusively within the highest priority sites,
estimates of the amount of habitat needed to attain our population
objectives will likely be underestimates. Nonetheless, explicit habitat
objectives based on population-habitat relationships enable us to convey
to policy makers and stakeholders the extent of actions required to
conserve populations. While some deviation from our strategy is
inevitable, close adherence to it by limiting our conservation actions
to priority landscapes will help ensure that our habitat objectives,
while minimal, come close to providing the anticipated population
response. Timely adjustments to habitat objectives can be made based on
recent management accomplishments, new scientific information, and other
influences on habitat due to policy changes and socio-economic factors.

***Designate priority areas --*** Priority areas can only be delineated
in the context of explicit objectives or goals. "Show me the best areas
for conservation" is not a satisfactory question on which to base
conservation assessment. Since no site is likely to actually have high
value for every species, some interpretation of relative priority is
necessary. "Show me the set of sites with the greatest management
potential to affect species X" is a much more appropriate question. Even
more likely is the question: "Show me the set of sites with the greatest
aggregate potential to affect species X, Y, and Z." Moreover, different
partners will often be most interested in benefiting different
combinations of species. Thus, while it may be possible to designate a
single set of priority areas for a specific program, it is seldom
practical for conservation partnerships. This is why developing a
portfolio of focal species^x^treatment SEMs is important. Once created,
SEMs can be rapidly combined to match the unique priorities of programs,
agencies and partners, i.e., a portfolio of SEMs provides a rapid
response capability to inform management.

Multiple species-specific SEMs may be integrated to assess the relative
potential of each unit of the landscape to yield aggregate population
benefits consistent with unique program, agency and partner priorities.
Caution must be used in combining SEMs because prediction errors
compound in the overlay process and because not all species that could
occur at a site have compatible habitat or management needs. Before
combining SEMs, it is necessary to (1) know what species or
environmental benefits a program emphasizes the most; (2) know what
treatments can be employed under a program; and (3) to thoughtfully
integrate SEMs based on management compatibility.

*Biodiversity and* *Species Richness Maps. \--* Although no single
standard definition exists for biodiversity, it is commonly interpreted
as the totality of genes, species, and ecosystems of a *region*. Thus,
concepts of biodiversity management have little utility at the pixel,
patch or management project scales at which SHC occurs. Instead,
conserving biodiversity requires balancing the area and configuration of
habitats needed by the full array of species within an ecoregion.
Biodiversity indices are often implicitly emphasized over species-based
approaches to strategic conservation (Simberloff 1998). However, rather
than a one-size-fits-all approach to program delivery, we believe that
the appropriate approach is to manage tracts such that ecoregional
biodiversity is conserved, with each agency contributing to biodiversity
conservation consistent with its specific conservation mandate and
priorities.

Contributing to the conservation of biodiversity is undeniably a high
priority; however, strategic habitat conservation is founded on being
explicit, measurable, and communicable. Unless a measurable and
universally acceptable definition of biodiversity can be developed, it
can not be described in a mission-based objective. Because explicit
definitions of biodiversity are elusive (Wilson 1997), other measures
like species richness are often equated to biodiversity management
potential. Maps of species richness are easy to produce using modern GIS
techniques. Species richness maps are commonly based on data such as
range maps or species occurrence. Abstract goals such as maximizing
species richness at patch scales is inappropriate, as implementing plans
that emphasize high local diversity can reduce overall (gamma) diversity
(Noss 1987) and are of little use for programs that typically have a
species or guild focus. Maps of species richness are likely to identify
ecotones, mountains and river corridors as priority areas because they
have greater habitat diversity although they are often poor habitat for
many priority species. We have the following concerns about maps of
species richness as they are commonly portrayed:

1.  Occurrence data are notoriously subject to errors - particularly
    errors of omission, that is, where no one has looked for a species.
    This is particularly true for uncommon, candidate or listed species;

2.  The approach is not founded on explicit objectives or predictions of
    population response -- the process is not dependent on models and
    there normally are no benchmarks against which to compare
    accomplishments;

3.  The approach makes limited use of the biological foundation
    available for many species including factors that are limiting
    populations and thus

    a.  provides little information about how and where management can
        be effectively used for species recovery, especially using
        habitat restoration;

    b.  provides no means of estimating management effects on
        populations which is critical for targeting management and for
        estimating accomplishments; and

    c.  provides no foundation for assumption-driven research;

4.  Habitat heterogeneity is often the most important factor in
    determining species richness. Number of species and habitat
    heterogeneity are often poor predictors of the importance of a site
    for conservation.

5.  Management compatibility is often not explicitly accounted for. For
    example, both American woodcock (*Scolopax minor*) and cerulean
    warbler (*Dendroica cerulea*) may be assigned to mixed deciduous
    forest tracts although the two species respond very differently to
    stand age and common forest management practices.

6.  Estimates of species richness are scale-dependent and common scales
    of assessment (e.g., large hexagons, hydrologic units, or counties)
    are much larger than the scale at which management decisions are
    routinely made. The implicit message is that habitat management
    anywhere within the geographic unit will provide equal benefits to
    the full array of species. This assumption is usually unwarranted.
    Inferences resulting from assessment at fine scales (e.g., 30-m
    pixels, 16-ha parcels) can be generalized to larger geographic
    units, but coarse scale assessments cannot be broken down to make
    fine-scale inferences.

For these reasons, maps of species richness within hydrologic units or
counties are not useful tools for strategic habitat conservation.
Nevertheless, maps of species richness are often compelling, as is the
misperception that they are surrogate predictions of biodiversity. As
such, they can inadvertently impede more sophisticated approaches to
conservation assessment, based on a critical assessment of trust
responsibilities, program authorities and priorities, population
objectives, limiting factors, management compatibility and spatial
scales. Although single-species planning and management seem to be
falling out of favor in the scientific literature, developing a
portfolio of species-specific assessment products enables a rapid
response to requests to designate priority areas tailored to a program's
unique authorities and priorities, including species priorities.

**Evaluation**

A basic principle of SHC is that our understanding of ecological systems
is never perfect. Models force us to make assumptions about limiting
factors and their effects on populations. The advantages of an iterative
process of SHC are two-fold with respect to science. On one hand, the
overall process is a systematic means of expressing what we believe
about how populations relate to their habitats and management at local
and landscape-scales. However, science is primarily a means of learning.
The scientific method is founded on articulating hypotheses (assumptions
in the planning process) and then setting out to try to disprove them
(evaluation through research). Without monitoring and research,
strategic habitat conservation is not an iterative process by which
managers learn and increase their effectiveness. Assumption-driven
research and outcome-based monitoring must be carried out to evaluate:

1.  Assumptions made about limiting factors in population-habitat models
    and SEMs;

2.  Effects of management on habitat and individuals;

3.  Program and agency impacts on population; and

4.  Net progress toward population objectives.

***Assumption-driven Research. --*** Although knowledge of highly
complex ecological systems will always be incomplete, agencies must
still make management decisions using the best information to guide
their actions. By systematically applying the biological foundation in
SHC we highlight uncertainties in the biological foundation for
management. In the absence of perfect knowledge, it is necessary to make
assumptions which are essentially testable hypotheses about
uncertainties. However, not all assumptions are equally important. We
may consider each in light of two factors: (1) how uncertain it is; and
(2) to what extent better information would affect future management
decisions. Assumptions that are both tenuous and high impact are
priorities for research. For example:

> *[Scenario 1]{.underline}* -- Research shows that soybean fields are
> used extensively by greater prairie chicken (*Tympanuchus cupido*)
> broods, even in the presence of adjacent native grasslands. Soybeans
> are superabundant at this time in the vicinity of grasslands used by
> prairie chickens, but soybean distribution varies annually.
>
> Assumption: Soybeans are a preferred habitat for greater prairie
> chicken broods.
>
> Conclusion: Limited uncertainty with little decision making value of
> better information because of high but annually variable soybean
> abundance driven by market forces.
>
> *[Scenario 2]{.underline}* -- Ornate box turtles are known to burrow
> extensively in sandy soils but surveys are limited. There are
> presently no plans for box turtle releases or reintroductions.
>
> Assumption 2: Ornate box turtles have a relative density at sites with
> sandy loam soils that is 200% greater than their density at sites with
> clay soils.
>
> Conclusions: Considerable uncertainty, but little value of additional
> information unless long-range management plans suggest releases or
> reintroductions will be necessary to sustain populations.
>
> *[Scenario 3]{.underline}* -- Dabbling duck daily nest survival rates
> have been shown to co-vary with percent grass (+) and cropland (-) in
> the landscape (Greenwood et al. 1995, Reynolds et al 2001).
> Unfortunately, the relationship is highly variable and its exact
> nature has been difficult to ascertain.
>
> Assumption 3: Waterfowl nesting success increases linearly with the
> percent grass within a 2-mi radius of a nest site (Reynolds et al.
> 2001).
>
> Conclusions: Considerable uncertainty and considerable value of better
> information because millions of dollars are spent annually to protect
> grassland for wildlife and millions more are spent to restore
> grasslands through programs like the Conservation Reserve Program.

Among the three hypothetical assumptions, Assumption 3 is the highest
priority for research because of its degree of uncertainty and the
potential benefits to wildlife populations of obtaining better
information. Assumption 3 may be restated as at least four competing
hypotheses (Fig. 6):

H~O~: Nesting success and percent grassland are independent

H~A1~: Nesting success and percent grassland are positively and linearly
related (the current assumption)

H~A2~: Nesting success and percent grassland are positively related but
the relationship is exponential

H~A3~: Nesting success and percent grassland are positively related but
the relationship is non-linear and reaches an asymptote at about 20%
grassland in the landscape.

If you are working exclusively within landscapes with \<20% grasslands
the value of better information is minimal because all three models
predict similar nesting success. However, the implications of obtaining
better information about this relationship when working in landscapes
with 20% or more grassland are huge. If the relationship is linear,
restoration of grass in any location will yield the same incremental
increase in nesting success. If curve b (Fig. 6) more accurately
describes the relationship, an agency should invest all of its grassland
protection and restoration resources in a few sites until the entire
landscape is grassland or nest success approaches 100%, whichever comes
first. If curve c (Fig. 6) is the best fit, an agency should add grass
to locations within landscapes with 20-35% grassland. Above 35% the
curve reaches an asymptote and we should move on to other areas because
additional grassland restoration will have less and less effect on
increasing nesting success. If the null hypothesis (H~O~) can not be
disproved, grassland protection and restoration would not seem to be a
very effective treatment for increasing nesting success.

When research priorities are established as an outcome of biological
planning, we are targeting mission-critical research, not simply
indulging our intellectual curiosity. Thus model-based biological
planning helps an agency articulate its research priorities. Moreover,
model-based biological planning is the means by which research results
find their way into conservation decisions.

***Outcome-based Monitoring. --*** An agency needs to make three types
of inferences about its resource management actions:

1.  The effects of a particular type of management action on habitat and
    individuals;

2.  Program and agency accomplishments expressed in terms of population
    impacts; and

3.  Net progress toward population objectives.

*[Assessing the Effects of Management.]{.underline} **\-- ***To evaluate
whether management actions are having the predicted consequences it is
necessary to monitor actual outcomes. This consists of answering two
basic questions: did the management action yield the expected habitat
response; and, did the change in habitat evoke the expected species
response? Answers to the first question enable managers to adjust their
tactics to more consistently achieve desired habitat conditions. The
second question is the means whereby we refine our models of
species-habitat relationships. This means that monitoring programs
should be structured around the same ecoregions as biological planning
to insure efficient model updating. It may not be necessary to monitor
the outcome of every management action, but monitoring outcomes at a
subset of management sites is essential.

*[Assessing Agency Accomplishments]{.underline}**.\-- ***Populations
vary in space and time in response to a variety of short-term,
uncontrollable environmental and anthropogenic factors. Population
status and trend estimates tend to have high variances because of
limited sample sizes and cyclic or short-term environmental variations.
Consequently, except for assessing long-term trends, actual counts of
individuals often have little utility for assessing accomplishments.
Rather than using highly variable, periodic counts of individuals,
models used to target management can be used to estimate population
impacts of management that actually occurred (Fig. 7). The sum of the
estimated impacts of each management action is an agency's
accomplishments. In other words, population monitoring is used to
indirectly assess accomplishments, with model refinement and estimation
as the intermediate step. This approach to accomplishment reporting has
two practical implications:

1.  Program accomplishments will be expressed in the same terms as
    population objectives, as well as acres and dollars; and

2.  Managers should report their annual accomplishments in terms of
    predicted aggregate population effects. Overall agency
    accomplishments are the sum of the output of individual managers.

*[Assessing Net Progress Toward Population Objectives.]{.underline}
**\-- ***Net progress toward population objectives is a function of
habitat gains versus losses, both of which may be driven more by
socio-economic or long-term environmental factors than by agency
accomplishments. Just like assessing agency accomplishments, assessing
net progress toward population objectives is a model-driven process.
Essential field data collection consists of (1) site-scale data on
species response to habitat codified in models, as noted above; and (2)
ecoregional, national, or continental data on habitat abundance,
distribution, and quality (e.g., from regularly updated land cover
data). Most broad-scale (national or continental) population monitoring
has not compiled data on habitats, with little effort to systematically
monitor population responses to habitat at site-scales. Although
continued broad-scale monitoring of populations is generally warranted,
interpreting annual changes in population status at any scale benefits
from the simultaneous collection of habitat covariates at site and
landscape scales upon which population estimates can be conditioned.

**Summary**

> We believe that SHC can make an agency that is mandated to conserve
> populations:

1.  More efficient at habitat management -- a function of being able to
    estimate biological benefits relative to management costs;

2.  More transparent and defensible because its actions are based on a
    systematic application of the best available science;

3.  More strategic in allocating its limited research and monitoring
    funds;

4.  More compelling at communicating the magnitude and nature of the
    conservation challenges and the strategies proposed to address them;

5.  More accountable; and

6.  More wide-reaching in informing other agencies and policy makers
    contributing to greater leadership in the conservation of trust
    species.

Although from time to time the focus may shift from one element to
another, SHC is a continuous iterative process of overlapping elements
that occur both sequentially and simultaneously:

1.  Biological Planning -- Assembling the biological foundation for
    conserving trust species -- including identification of priority
    species and a subset of focal species; designation of population
    objectives; and compilation of models that describe expected focal
    species-habitat relationships;

2.  Conservation Design -- Applying models to spatial data that
    culminates in the designation of priority management areas and
    coarse estimates of the amount of habitat that will be needed to
    attain a suite of population objectives;

3.  Conservation Delivery -- The implementation of management actions
    with the goal of efficiently affecting populations;

4.  Assumption-driven Research -- Evaluating and refining biological
    planning assumptions; and

5.  Outcome-based Monitoring -- Assessing the effects of management on
    habitats and individuals to make inferences at multiple scales that
    have a bearing on our future management decisions.

Conservation strategies are dynamic suites of objectives, tactics and
tools that change as new factors or information influence the system.
The very act of doing assumption-driven research and monitoring implies
a commitment to continuous re-planning using better information about
how a species responds to its habitat and our management actions.
Furthermore, external forces operate on habitats and populations and our
strategies must acknowledge their effects on the attainment of our
objectives. The SHC framework is designed to promote learning about
populations and how they respond to habitat from the process of habitat
management. By following the cycle of planning, doing and evaluating
described as SHC, we continuously move toward more and more reliable
management decisions. The elements of conservation strategies --
objectives, tactics, spatially-explicit models of priority areas,
monitoring programs, etc. -- are all subject to change as new
information becomes available or new forces operate on the system. To
paraphrase a cliché that is particularly relevant to SHC, "the only
thing that's constant is change."

**LITERATURE CITED**

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List of Figures

Figure 1. The basic strategic habitat conservation (SHC) cycle.

Figure 2. A schematic including many of the important elements in the
iterative SHC approach to conservation.

Figure 3. A hypothetical relationship between interior forest breeding
bird recruitment rate and the ratio of forest patch edge:area.

Figure 4. General relationships between usefulness and cost for (A)
models predicting various types of biological responses and (B) methods
of developing models.

Figure 5. Grassland bird conservation potential assessed for a series of
1-ha pixels (top) and at a county scale (bottom) and a hypothetical
grassland acquisition. County or large watershed-scale assessments make
the unreasonable assumption that the entire area has relatively
homogeneous conservation potential, while the smaller scale assessment
units can be aggregated to make a more reliable prediction of population
benefits.

Figure 6. Alternative relationships that describe a general trend of
increasing waterfowl nesting success with increasing amounts of grass in
a 3.2-km radius landscape. Each implies the need for a different
grassland restoration strategy.

Figure 7. Models used to target habitat management practices such as
wetland restoration like a regression for breeding ducks (Example D)
also may be used to estimate accomplishments in terms of population
impacts. Crosshatched features are drained wetlands and solid features
are restored wetlands in a 10-km^2^ western Minnesota landscape. The
same regressions used to target management in Example D may be used to
estimate the increase in capacity to support breeding pairs attributable
to the restoration of individual wetlands. In total, these represent the
agency's accomplishments toward its objectives (and mission) over time.

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