Landscape Disturbance-Succession Simulator

Tech Details & Access

Learning from History to Guide Restoration

The Landscape Disturbance-Succession Simulator (LDSim) shows that forest stewardship can restore historical conditions in far fewer than 100 years—the time it would otherwise take for nature to take its course if natural disturbances were permitted to resume.

LDSim simulates historical fire and forest growth before Euro American settlement as a baseline reference for ideal forest conditions.
LDSim helps land managers reduce high severity fire by indicating which areas are furthest departed from the reference period.

What is LDSim?

The LDSim model was developed by Dr. Kevin McGarigal to simulate the processes of disturbance and succession in the fire-adapted forests of the Northern Sierra during a 300-year historical reference period, circa 1550-1850.

LDSim can also help determine when future conditions might be forcing the system to operate outside its natural range of variability and inform pathways to action at multiple scales, from the treatment unit to the planning level.


The LDSim model has been calibrated to the Northern Sierra ecoregion, totaling over 8,000 square miles or 5 million acres. Much of the Sierra Nevada is federally-held public land, managed by the Forest Service, Bureau of Land Management, and National Park Service.

LDSim data have been applied in the 275,000-acre North Yuba Forest Partnership project area to help develop treatments and prioritize locations that restore forest conditions closer to their natural range of variability.

Square Miles
Million Acres

Northern Sierra Ecoregion Focal Watersheds.

For modeling purposes, the Northern Sierra ecoregion was divided into eleven landscapes corresponding to major watersheds. The Middle and North Forks of the Feather River watersheds were divided into “westside” and “eastside” watersheds based on physiographic differences, despite the fact that the entire Feather River watershed flows westward.

Current Data Extent

Six Watersheds of the Tahoe Central Sierra Initiative

So far, LDSim has been modeled for the six watersheds corresponding to the extent of the Tahoe Central Sierra Initiative (TCSI) area of interest.

Yuba River Watershed

within current LDSim project area

LDSim data were applied within a section of the Yuba River landscape to inform treatments and prioritize locations for forest restoration.


within Yuba River Landscape

LDSim is modeled at the subbasin level with geophysical units at a mean size of just over 14,000 acres.

GPU’s (Geophyiscal Units)

within Yuba River Landscape

LDSim is also modeled at the geophysical unit (GPU) level with geophysical units at a mean size of 50 acres.

BPC's (Biophysical Classes)

within Yuba River Watershed

LDSim is also modeled at the biophysical unit (BPU) level, which maps site productivity (predicted cumulative tree biomass under historical dynamic equilibrium) into four biophysical (BPC) classes, ranging from high to low. BPU/BPC data informs HRV and current departure and is aggregated into site/subbasin data.

Future and Current LDSim Data Coverage

within the Northern Sierra Ecoregion

VP Data Commons aims to expand the LDSim project area within the Northern Sierra. Get in touch to discuss application in your region!

Are you interested in applying LDSim within your region?

Contact Us

We want to hear from you! In partnership with VP Data Commons, Dr. Kevin McGarigal is actively looking to model LDSim for other watersheds in the Northern Sierra and beyond that could benefit from data-informed restoration.

LDSim has a relatively open architecture for defining disturbance and succession processes that make it available to unique applications and scenarios. LDSim’s Northern Sierra project area incorporates wildfire disturbance, alone, but other applications could involve a combination of fire, wind and insect disturbances, to be applied in the Northern Sierra and beyond.

Dynamic Equilibrium in the Historical Northern Sierra Landscape

Since the 1980s, ecologists have increasingly adopted a dynamic view of ecosystems in which ecological processes of disturbance and succession create a range of uniquely shaped places.

For thousands of years prior to Euro-American settlement, the Sierra Nevada landscape was in dynamic equilibrium with indigenous stewardship and climatic, topographical, and ecological factors, resulting in a high level of resilience to major ecological change.1

Disturbance events—such as landslides, tree windfall, and fire—are ecological influences on the establishment, growth, and succession of vegetation in a landscape over time.
Contemporary forest management activities such as mechanical thinning and prescribed burns reduce forest density by removing small or unhealthy trees while leaving healthy, mature trees standing, resulting in a landscape closer to ecological dynamic equilibrium and historical conditions. Photo by Bureau of Land Management Oregon and Washington.

Fire was the major and regular source of disturbance in Sierran forests.2 Fires caused by both lightning and cultural burning were exceptionally frequent but low intensity surface fires, resulting in all parts of the landscape experiencing fire on a rotation of at most 30 years.3

Fire Rotation Period, or Interval (FRI): The average number of years between fires within an ecosystem.4

Using LDSim Modeling to Quantify the Departure of Contemporary Forests from Historical Conditions

The arrival of Euro-American settlers in the Sierra Nevada led to sweeping ecological changes that now have greatly altered many Sierran landscapes through fire exclusion, grazing, road-building, timber cutting, and recreation.5,6,7,8

Historically, high-severity fire rates were low enough to allow most stands to succeed into late-development and old-growth conditions characterized by a variety of (mostly open) canopy structures.9,10,11,12

High Mortality Fire is considered greater than 75% overstory canopy mortality.13,14
The 2021 Dixie Fire burned 963,309 acres (or 1,505 square miles) in the Northern Sierra and caused unprecedented levels of moderate to high vegetation mortality across a significant portion of the fire’s footprint. Photo by Georgia Reid.

LDSim data is intended to guide forest restoration activities that help prevent future large, high-severity fires from occurring. To accomplish this, the LDSim model simulates the historical range of variability (HRV) of fire disturbance and vegetation succession during a user-determined historical reference period—for example, the years 1550-1850, to represent ecosystem conditions before Euro-American settlement.

HRV data is helpful because it offers a baseline reference for the natural range of variability (NRV) of a landscape. Using HRV, scientists can look back to the last known period during which dynamic equilibrium existed and aim to restore the current forest closer to those conditions.

To be applied, LDSim quantifies the current departure of forest conditions from HRV. LDSim departure data help land managers prioritize forest treatments when resources are limited.

Comparing current conditions to HRV helps us understand that the changes caused by disturbance events are not problems to be suppressed but rather are integral to the resilience of an ecological system.

Natural and Historical Range of Variability

The concept of Natural Range of Variability (NRV) guides most current land management activities on National Forests and other public lands today.

First applied in ecosystem management for biodiversity and endangered species in the 1990s, NRV appeared in the 2000 Forest Service Planning Rule and is prominent in the current 2012 regulations (finalized in early 2015).

Natural Range of Variability (NRV): The ecological conditions, and the spatial and temporal variation in these conditions, that are relatively unaffected by people, within a period of time and geographical area appropriate to an expressed goal (Landres et al. 1999).
“Plan decisions affecting ecosystem diversity must provide for maintenance or restoration of the characteristics of ecosystem composition and structure within the range of variability that would be expected to occur under natural disturbance regimes of the current climatic period” (36 CFR §219 2012).

Why Simulation?

In landscapes like the northern Sierra Nevada that have been drastically altered by settlement and the suppression of historical fire regimes, we can’t observe conditions in which fire suppression is not part of the equation.15

LDSim uses simulation to generate a detailed dataset that paints a picture of how far current forests are from historically normal conditions.

In the absence of consistent and complete data, LDSim simulates wildfire disturbance and generates a detailed HRV and Current Departure dataset that paints a picture of the magnitude to which current disturbance and succession processes differ from historic ranges.16,17,18

Models for simulating NRV, HRV, and future range of variability (FRV) have proliferated since the early 1990s. By 2004, some 45 LDSMs alone had been developed,19 many of which are still in use today.

LDSim data is a statistical summary. While LDSim mitigates the extrapolation errors of alternative approaches, LDSim’s weakness is that the results can be unintuitive on the observable landscape—underscoring the importance of supporting land managers in understanding how to interpret and apply LDSim results.

Within the western US, the Rocky Mountains and Oregon Coast Range in particular have been the focus of several simulated HRV studies, while possibly only one has been conducted in the Sierra Nevada, which took place in Sequoia National Park in the southern Sierra before the turn of the century.20

Management Applications

The 8,000+ square mile Northern Sierra ecoregion in California is a dynamic mosaic of ecological systems driven by the interplay of disturbance regimes—especially fire.

Understanding the natural and historical disturbance and succession patterns is essential to planning and managing this landscape for desired conditions while navigating tradeoffs and limitations.

A Note on Scale: There is no single “right” scale for characterizing NRV or HRV. For LDSim, Dr. McGarigal focused on choosing a set of scales that would capture the ecological patterns most relevant to land management.

1. Using Fire Return Interval to Restore Good Fire

LDSim helps land managers reduce the risk of high severity, high mortality fire by illustrating which areas of the landscape may be most departed from the historical fire rotation period.

Observing a map of LDSim mean Fire Return Interval (FRI) data is like peering into the past; FRI shows how most parts of the landscape experienced on a rotation of at most 30 years.

To easily implement FRI data in decision making, given practical limits on the number of acres that can be treated over some period of years, LDSim’s Fire Return Interval Departure (FRID) data show where restoration to promote the return of good, low intensity fire is most urgently needed.

In the FRID map, higher positive values (red to orange) represent areas where fires are returning more frequently than in pre-settlement times, and are therefore priority candidates for fire management resources. Conversely, negative values (light to dark green) denote areas where fires are returning less frequently than historically modeled, and could be considered a lower treatment priority.

Fire Return Interval (FRI) data represents the average number of years that fire events historically occurred on the landscape.
The above map presents Fire Return Interval Departure (FRID) data for the Yuba River watershed, grouped by GPU. This data shows how often current fires (from 1909 to the present) occur compared to simulated historical range of variability (HRV) frequencies. A higher FRID value (yellow to red) indicates a more significant deviation from past conditions, suggesting that the landscape is experiencing fewer fires than in the past. Understanding which areas have the most significant departure helps wildfire management prioritize where prescribed burns are urgently needed to restore areas to their historical fire frequency.

2. Using High-Mortality Fire Return Interval to Manage for Habitat

High-mortality Fire Return Interval data can be used as a proxy to guide land managers to focus restoration for the greatest impact on promoting biodiversity and habitat.

To provide habitat for numerous early-seral dependent species—such as birds and deer that thrive and forage in meadows and forest openings—land managers can look to shorter high-mortality FRI areas as logical priorities for creating and maintaining large forest openings that have a greater likelihood of persisting in early-seral forest conditions under natural fire disturbance regimes.21,22

By contrast, if managing habitat for late-seral dependent species, such as the California spotted owl (Strix occidentalis occidentalis), areas with a longer high-mortality FRI might be managed for large trees and old growth conditions as they have a greater likelihood of persisting in late-seral forest conditions under natural fire disturbance regimes.

High Mortality Fire Return Interval (FRI) data provides a proxy for how well vegetation may persist in either an early or late seral stages as modeled under dynamic historical conditions.

3. Using Geophysical Unit (GPU) Level Composite Departure to Inform Treatment Units

At a mean size of 50 acres, GPU’s are best suited for identifying and prioritizing local areas for restoration treatments. GPU-level Composite Negative Departure Score is used for determining the direction and magnitude of change needed to move a selected unit towards historical reference conditions.

GPU data can be queried to create subsets of interest—and, with 11 variables each with 3-5 classes, there are 393,660 unique queries possible when using LDSim!

The composite negative departure score at the GPU-level is the result of combining and weighing the eleven individual departure metrics outputted by LDsim within each GPU unit. The final numeric values represent the percentage of maximum departure of current landscape metrics from the GPU’s historical modeled range of variability (HRV). Larger values indicate increasing degrees of departure.

4. Using Subbasin Unit Level Departure to Inform Forest Planning and Prioritization

There are six key attributes and departure scores at the subbasin level related to forest openings, developmental stage, tree size and percent large trees. These layers can inform specific management priorities.

For instance, using the percent large trees departure index, land managers can see the extent to which the contemporary landscape drastically differs in the extent of large trees where coloration is yellow to red indicating moderate to high departure.

Alongside GPU level high-mortality FRI data (mentioned above), the large trees departure index could inform forest planning and treatments aimed at increasing the number of large trees to benefit late-seral dependent species like the California Spotted owl.

The Subbasin level composite departure score incorporates all subbasin attributes to indicate areas in overall greatest need of attention. Alongside GPU level results, individual biophysical units (BPU) departure results can be used to inform forest treatments with the general goal of restoring HRV.

The Percent of Large Trees (>30in dbh) Departure Index data identifies Subbasins and BPCs where fewer or greater numbers of large trees are present within the landscape relative to HRV. The departure index reveals the level and degree of departure from the historical range: yellow to red values indicate that the current landscape has fewer large trees than the HRV. Conversely, the range of green values show more area in large trees than the median HRV. To this point, the map above highlights that most of the Yuba River landscape has far less area in large trees than would be expected under the historical reference period.
The composite negative departure score by Subbasin-by-Biophysical Class (BPCs) is the result of combining and weighing the six individual departure metrics outputted by LDsim within each Subbasin-by-Biophysical Class. The final numeric values represent the percentage of maximum departure of current landscape metrics from the GPU’s historical modeled range of variability (HRV). Larger values indicate increasing degrees of departure.

Applied Use Case

North Yuba River watershed
Management Teams
Forest Service Interdisciplinary Team (ID Team) and the North Yuba Forest Partnership (NYFP)
Data Used
North Yuba River watershed

LDSim has been utilized in the North Yuba River watershed to assist the Forest Service Interdisciplinary Team (ID Team) in developing proposed actions and proposed amendments on the Tahoe National Forest. 

The North Yuba Landscape Resilience Project is a 275,000-acre forest restoration project in Northern California where project priorities included watershed health, wildlife habitat, recreation, and resilience to drought and fire.

The project team used departure metrics to estimate landscape conditions for characterizing impact of treatment. Historical range of variability (HRV) served as the primary reference condition range for natural range of variability for a host of different departure metrics, with the Landscape Disturbance-Succession Simulator (LDSim) used to compute HRV of landscape processes, and forest structure and composition. A composite forest structure departure index was used to characterize potential drought intensity and risk to resources and assets. 

HRV-informed fire return interval departure (FRID) was used to characterize ecological functional health of various natural resources. Both departure metrics and a host of other geospatial datasets were ultimately used to calculate the Restorative Return on Investment (RROI) metrics that were used for initial project area development.

Treatment prescriptions included ecologically-based thinning and prescribed fire to reduce the density and continuity of tree cover and understory vegetation. 

Reducing tree density decreases competition for water among the remaining vegetation that increases resistance to drought and overall water supply, while reducing ladder fuels and fuel loads of drought-stressed trees lowers the risk of high-mortality canopy fires.

Informed by HRV, the restoration project was also designed to promote diversity in the sizes, ages, and species of trees, to strengthen habitat and support key native species. 

Through LDSim-informed restoration treatments, the NYFP aims to bring the project area into closer alignment with the historical and natural range of variability of wildfire disturbance by restoring a mosaic of meadows, oak woodlands, and mixed conifer forest to the Yuba River landscape. 

To read more about the goals, treatments, and innovative finance strategies by North Yuba Forest Partnership that were informed by LDSim data, explore their Story Map.

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