**Forecasting Landscape Change: Toward a System of Prediction and
Monitoring**

**Issues/Problems**

Landscapes and their associated ecosystems are shaped by complex and
interrelated biological, geological, and hydrological responses to both
natural and cultural influences. The response and resiliency of
landscapes is affected by factors that vary over space and time. Slow
changes in local habitat and population eventually exceed thresholds
that may bring on rapid changes in regional aquatic and terrestrial
ecosystems. Increasing and competing pressures for land-use, demands for
water use, rapid spreading of invasive species and wildfires are causing
dramatic and perhaps irreversible changes to our public lands. These
changes can be either muted or magnified by natural climate variability,
or anthropogenically-driven climate change. Understanding the
sensitivity of landscapes to change, and forecasting the rates, types,
and outcomes of such change, pose important integrative scientific
problems of direct societal relevance. Improvements in modeling and
forecasting landscape change are needed to inform better decisions in
resource and land management.

**Stakeholders:**

The seven DOI Bureaus are the stakeholders and collaborators most
supportive of a Forecasting Landscape Change research effort. USGS
science is also critical to EPA, Corps of Engineers, Dept of Agriculture
(including USFS and NRCS), Department of Energy, several DOD branches,
and other principal land management or regulatory agencies. Finally, our
science is important to the management and regulatory agencies for the
States, local communities, NGOs, private landholders and regional
watershed management entities; and finally the academic community.

**Opportunities for Integration**

Predicting landscape change requires new integrated science aimed at
understanding complex interactions of natural systems. This integrative
scientific niche, including complex systems modeling, should be a focus
of USGS programs. Notable USGS capabilities with integrative potential
are natural hazards, land-use change, habitat integrity, biodiversity
and non-indigenous invasive species, fire fuels, sedimentation and
stream-flow histories, and investigations into global climate change.
Additionally, cross-Bureau capabilities span the scientific fields of
environmental isotope chemistry, remote sensing, biogeochemical/nutrient
cycling, vegetation and geologic mapping, ground-water and surface water
modeling, sediment transport modeling, animal and vegetation population
modeling, and other biological, hydrological, geological and social
science endeavors. Scientific support includes analytical labs, database
management, GIS science, remote-sensing data analysis, visualization
techniques, and science policy analysis.

**Approach**

Landscape forecasting can be used to solve many land and resource
management issues. Whereas some management issues can be resolved at
local or regional scales, others will require information across a range
of scales. As scales change, the data required to develop landscape
forecast models will change, as will the type of answers available from
these models. The following examples illustrate this point:

*Small watershed scale*

Example problem: The road access to a major feature in a national park
goes through a riparian area, crossing a stream at multiple points .
Some user groups want the road closed to protect habitat, others want it
improved. The park manager needs to understand the implications of each
of these actions.

Data needs: Site level data (e.g., vegetative cover; stream chemistry,
temperature, and biota; hydrologic regimes, (times and amounts of water
flow) and surficial geology) and how these are altered by vehicle
traffic and road type (e.g., wildlife response, flooding regimes; water
chemistry), records of past changes in the watershed

Model outputs: How vehicles and roads affect selected indicators (e.g.,
effect of timing and number of vehicles on wildlife; effect of road type
on land surface stability and sediment production)

*Landscape scale*

Example problem: The state has transplanted elk onto USFS lands. The elk
move onto BLM lands and appear to compete for forage with livestock,
trample soils, and drive deer down into the adjacent national park
lands.

Data needs: Landscape level data (e.g., vegetation distribution in the
watershed relative to geology; movement and forage utilization patterns
of elk, deer, and livestock; stream sediment load throughout the
watershed and response of aquatic invertebrates), maps of biological
soil crusts and other stabilizing features, climate model, records of
past changes such as quantitatively compared remotely sensed images

Model outputs: How the presence and numbers of elk affect selected
indicators (e.g., soil erosion, vegetation, livestock performance); and
how changes in browse might affect their distribution in the future due
to climate variations

*Regional scale*

Example problem. Sage grouse, which depend on certain native grasses,
are declining throughout the western U.S.

Data needs: Regional level data for species and habitat management
(e.g., distribution of sagebrush types and native grasses and their
condition throughout the West relative to geomorphic surfaces and soil
types, definition of high quality habitat for sage grouse and mapping of
these qualities, information on invasive species that compete with
grass, sources of surface water, land ownership and use, road network)

Model output: How much high quality sage grouse habitat is left? Where
is it located? What threatens it in the short and long-terms? How will
it change with time under future climate scenarios and land-use
scenarios?

### Alternative regional scale 

Example problem: Urban, suburban, and exurban expansion is increasing
into fire-adapted forests and riparian valley bottoms throughout the
western US. Development initiates a host of natural resource
consequences, from increased sediment and runoff into streams from road
and homesite development, to reduction of native species from predation
by house pets, habitat alteration and fragmentation, opportunities for
non-native species encroachment, and increased local emissions of air
pollutants.

Data needs: Maps of fire fuels, fire frequency and vegetation and land
cover along the urban-wildlife interface are needed. Socio-economic data
on changing demographics including cultural and economic drivers are
needed

Model output: Outputs include predictions of urban/suburban expansion,
predictions of plant and animal changes, and the projected rate and
intensity of fire based on a suite of climate/weather and land use
scenarios. Urban growth contagion-type models that show where
development is most likely to occur (useful for local and regional
planners), hydrologic runoff models that show potential impacts to
streams from development (also useful a priori for planners and
developers), ecological models of species and population dynamics to
show potential change with land use change, and decision-support models
that can be used with decision-makers to optimize various development
plans.

### Continental and Global Scale

Example problem: Changes that are manifested at local and regional
scales may be due to global impacts. These include global changes in
vegetation seasonality due to global warming, such as the early arrival
of spring in the arctic/boreal zones, as well as large-scale changes in
human settlement due to the emigration from traditional "bread-basket"
farms. The drivers for the changes may be climatic, economic and
societal. We have the unique opportunity to look across scales to
determine the drivers of large-scale changes on the landscape. In the
past these changes were induced by technology such as of barbwire, and
policy and planning such as Public Land Survey. Current and future
drivers of change include the effects of tax and regulatory process, and
climate change.

Data needs: Continental and global land cover maps, remotely-sensed
species-specific vegetation cover (ideally as a time series), definition
of animal species habitat needs, maps of plant and animal species
distributions, climate, water and air quality data (including long-term
records), forest and rangeland management history, road network, land
ownership and use, assessments (or indicators) of current environmental
condition in order to measure future change.

Model outputs: Predictions of broad-scale changes associated with
climate and human impacts that can be used to drive regional and
watershed understandings of the changes.

Other scenarios that can be modeled with landscape forecasting include
identifying pathways of invasive species and their role in ecosystem
change; quantitatively tying climate-history records to changes in
vegetation and landscape processes; understanding and modeling
biogeochemical cycles; clarifying the geomorphic and biologic effects of
catastrophic events (fires, eruptions, earthquakes, and large storms);
developing remote sensing techniques for monitoring and quantitatively
characterizing key landscape attributes; developing models that predict
the interactions of vegetation with soils; and predicting the effects of
future climate and atmospheric chemistry changes on soil hydrology and
chemistry, surface discharge, and related ecological processes.

#### Opportunities and Common Threads

The common threads of Forecasting Landscape Change across disciplines
and regions include:

1.  Linking managers, planners and decision makers with scientists in
    settings that allow for two-way education, problem setting and
    problem solving.

2.  Linking models of hydrology, biology, geology and socio-economy to
    expand the scope of our understanding of complex factors influencing
    changes in the landscape, and to cost share in data acquisition,
    analysis and reporting.

3.  Development of indicators of change. The drivers of landscape change
    and their effects must be assigned probabilities, and effective
    monitoring and modeling tools put in place. Not all impacts are
    equally likely to occur and their effects may vary. Indicators allow
    scientists to focus on specific criteria and develop effective tools
    for tracking these indicators.

4.  There is a need for comprehensive forecasting, that involves
    developing and implementing appropriate methods for sampling,
    analyzing and reporting on the full suite of science issues: human
    and biologic health, water quantity and quality, soil, and natural
    and human-induced hazards.

***Annual Program Direction***

Recent guidance from OMB suggests high-level support for USGS science in
the support of DOI Bureaus, such as NPS, FWS, BOR, and BLM. Science that
can contribute to resolution of conflicts between multiple or competing
interests is also a priority of both DOI and DOA lands. Issues include
population growth into the wildland interface, habitat fragmentation and
its effect on wildlife, invasive species, wildfires, and ecosystem
health. Efforts should be directed at critical resource management
issues for the Bureaus.

***Select Geographic Opportunities at all scales:***

Small watershed

-   Ecosystem degradation related to mountaintop mining

-   Habitat fragmentation studies - Highlands Stewardship, New Jersey
    Pine Barrens, and Chesapeake Bay

-   Impacts of Urban growth on critical habitats and cultural resources-
    the I-95 National Park and Battlefields\' corridor

Landscape

-   Urban Earth: Southern California or Pacific Northwest

-   Urban Dynamics: Front Range of the Rockies

-   Mojave and Sonoran Desert -- Vulnerable Desert Ecosystems

-   The Arctic Coastal Plain and Foothills Ecoregions

-   Powder River Basin in Wyoming and Montana (coalbed methane)

-   Evolution of and impacts on estuarine habitats, Florida and Biscayne
    Bays, the mid-Atlantic and the Gulf of Maine

Region

-   Central and Southern Rockies- Ongoing drought, resetting of
    woodlands and forests by wildfires and insect outbreaks, and
    associated ecological and hydrological changes

-   Great Basin and Colorado Plateau

-   US/Mexico Borderlands -- competing water issues; fragile habitats;
    health;

-   Decreasing water quantity and quality and environmental impacts
    related to sediment flux, sea-level rise and population growth along
    the Atlantic Seaboard

-   Great Lakes Coastal Ecosystems

Continent

-   The Arctic Ecoregion

-   Tropical Rainforest Ecosystem -- Amazon

-   Antarctic Ecosystem

-   Habitat fragmentation -- Human and animal migration

Global

-   Desertification

-   Protected area planning

-   Soil and water conservation

-   Human Health

-   Global Change

**Conclusions and Recommendations**

Forecasting Landscape Change is an evolving priority focus of US
Geological Survey. To achieve a successful program, there are
significant challenges including how to prioritize changes that will
have a significant impact, determine our ability to monitor these
changes, develop an effective sampling frame and strategy with all of
the necessary statistically-sound sampling and reporting mechanisms.
More importantly, how might we unite our activities to actually achieve
meaningful forecasting? What might be the one achievable action that we
could undertake as a group that would have a tangible impact? Based on
our assessment, Forecasting Landscape Change requires some infusion of
resources and programmatic support to enable this inter-disciplinary
team to develop an operational program to undertake the mission that we
have outlined.

Forecasting Landscape Change should be an important priority of the US
Geological Survey. There are significant scientific as well as social
challenges to developing effective forecasting capability, but there are
also significant opportunities to provide input to very real complex
environmental issues. Challenges include determining how to prioritize
changes that will have a significant impact, and how to best monitor and
interpret these changes. More importantly, how might we unite our
activities to actually achieve meaningful forecasting? Opportunities
abound for applying integrated scientific understanding to local to
global scale problems ranging from species interactions to disruption of
major biogeochemical cycles. Forecasting Landscape Change will require
an infusion of resources and programmatic support to enable this
inter-disciplinary team to develop an operational program to undertake
the mission that we have outlined.