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

A 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

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.

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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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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.