Section 3
Modeling Historic Variation and Its
Application for Understanding
Future Variability
Robert E. l(eane
USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory, MT, USA
lthough some may doubt its usefulness in a
future with rapidly changing climates, exotic
introductions, and increased human land use,
the historical range of variation (HRV) of ecological
landscape characteristics provides a relatively useful
reference point for evaluating the impacts of landmanagement activities. Unfortunately, comprehensive
spatial and temporal data describing historical landscape conditions are rare for many areas, with most
information being limited in geographic scope and
A
relatively recent. The main problem facing many ecologists, scientists, and land managers is how to quantify
the HRV of landscapes in a format that is scientifically
credible, useful to land management, temporally deep,
and spatially extensive, while still being relevant in
today's changing world.
The best method for quantifying historical landscape
conditions relies on a chronosequence or a series of
maps or data layers from one landscape over many past
time periods. However, temporally deep, spatially explicit
Historical Environmental Variation in Comervation and Natu ral Resource Management, First Edition. Ed ited by john A. Wiens ,
Gregory D. Hayward, Hugh D. Safford, and Catherine M. Giffen.
© 2012 John Wiley & Sons. Ltd. Published 2012 by John Wiley & Sons, Ltd.
111
112
Modeling historic variation and its application
empirical chronosequences of landscape conditions
are rare because aerial photography and satellite
imagery were nonexistent before 1930, and paper
maps of forest vegetation are scarce and inconsistent
prior to 1900. Another method involves using digital
maps from similar landscapes. taken from one or multiple time periods, and gathered across a geographic
region to quantify the landscape HRV (Hessburg et aL
1999; 2000). This substitution of space for time
assumes that all landscapes used to define HRV are
similar in terms of environmental. disturbance. topography, and biological conditions. However. most landscapes are unique in terms of the biophysical
environment and the manifestation of disturbance
dynamics over time creates distinctive variations in
landscape HRV characteristics because of differences
in topography, orientation, wind direction. and many
other microclimate, biotic. and edaphic characteristics
(Keane et aL 2006).
In many situations. simulation modeling provides
the only viable source for generating comprehensive
HRV data. This third method involves simulating historical dynamics using landscape models to produce a
chronosequence of simulated spatial data to use as reference. This approach assumes that succession and
disturbance processes are simulated accurately in
space and time. Many spatially explicit ecosystem simulation models are available for quantifying HRV patch
dynamics (see Mladenoff & Baker 1999 ; Keane eta!.
2004), but many are computationally intensive, difficult to parameterize and initialize, and complex in
design, making them difficult to use across large
regions over long time periods. Even with these limitations, simulation models often provide the only way to
quantify HRV for many landscapes, and therefore, they
are a critical tool for managing today's landscapes.
Although spatial chronosequences are clearly preferable, simulated chronosequences provide a viable, and
in some cases, the only, alternative to creating HRV
data.
This section describes the use of simulation modeling to develop HRV chronosequences for land management. The first chapter deals with all the background,
issues, and limitations of creating simulated HRV time
series. Important topics include landscape size, selecting the most desirable model, and data parameterization issues. The next chapter provides examples of how
simulated HRV time series can be used in natural
resource management at various scales. Collectively,
these chapters may provide the information needed to
start an HRV project using a landscape simulation
model to generate historical time series, which can
then be used as a reference to compare management
treatment alternatives.
REFERENCES
Hessburg, P.F.. Smith. B.C. & Salter. R.B. (1999). A method for
detecting ecologically significant change in forest spatial
patterns. Ecological Applications, 9. 1252-1272.
Hessburg, P.F.. Smith. B.G .. Salter. R.B .. Ottmar, R.D. & Alvarado, E. (2000) . Recent changes (1930's-1990's) in spatial
patterns of interior northwest forests , USA. Forest Ecology
and Management, 136, 53-83.
Keane, R.E .. Cary, G.. Davies. I.D .. et al. (2004) . A classification of landscape fire succession models: spatially explicit
models of fire and vegetation dynamic. Ecological Modelling.
256. 3-27.
Keane, R.E., Holsinger, L. & Pratt. S. (2006). Simulating historical landscape dynamics using the landscape fire succession model LANDSUM version 4.0. General Technical
Report RMRS-CTR-171CD. USDA Forest Service. Fort
Collins, CO. USA.
Mladenoff. D.J. & Baker. W.L. (1999). Spatial Modeling of Forest
Landscape Cllm1ge. Cambridge University Press, Cambridge.
UK.
HISTORICAL
ENVIRONMENTAL
VARIATION IN
CONSERVATION AND
NATURAL RESOURCE
MANAGEMENT
Edited by
John A. Wiens
PRBO Conservation Science
Petaluma, CA, USA
School of Plant Biology
University of Western Australia
Crawley, WA, Australia
Gregory D. Hayward
USDA Forest Service
Alaska Region, Anchorage, AK, USA
USDA Forest Service
Rocky Mountain Region
Lakewood, CO, USA
Hugh D. Safford
USDA Forest Service
Pacific Southwest Region
Vallejo, CA, USA
Department of Environmental Science and Policy
University of California
Davis, CA, USA
Catherine M . Giffen
USDA Forest Service
National Office
Washington, DC, USA
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Historical environmental variation in conservation and natural resource management I edited by John A. Wiens ... [et al.].
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ISBN 978-1-4443-3792-1 (cloth) -ISBN 978-1-4443-3793-8 (pbk.) 1. Landscape ecology. 2. Natural resourcesCo-management. I. Wiens, John A.
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Front Cover: Great Basin bristlecone pine trees (Pinus longaeva) in the Patriarch Grove of the White Mountains, eastern
California. Bristlecone pines growing in the White Mountains are the oldest known trees in the world, with individuals
reaching ages upwards of 5000 years. Dry conditions in the White Mountains also result in exceptional preservation of
remnant wood. By cross-dating dead wood with living trees, the tree-ring chronology for the White Mountains extends back
almost 12,000 years. providing an exceptional example of a historical legacy. Photograph by Peter M. Brown, Rocky
Mountain Tree-Ring Research.
Cover Design By: Steve Thompson.