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.
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the processes of disturbance and succession in the fire-adapted forests of the Northern Sierra during a 300-year historical reference period, circa 1550-1850.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.
Landscape Ecologist, Vibrant Planet
Kevin is an applied research landscape ecologist, having earned a BS degree in Forestry and Wildlife from Virginia Tech, an MS. degree in Wildlife Science from Oregon State University, and a PhD in Forest Science from Oregon State University. He was a professor of landscape ecology at the University of Massachusetts, Amherst from 1997 until 2019. Kevin’s applied research focuses on understanding how landscapes are structured physically and biologically and the agents responsible for those patterns, how these patterns affect the distribution and dynamics of plant and animal populations, how these patterns and processes change over time, and how to apply this information to better manage natural resources over multiple spatial and temporal scales. Accordingly, a major component of his work has been the development and technical support of software tools for both scientists and managers (e.g., FRAGSTATS) for quantifying landscape patterns and landscape dynamics. In particular, Kevin is the developer of a landscape disturbance-succession simulation model (LDSim) for simulating the range of variation in landscape patterns under natural and anthropogenic disturbance regimes to help land managers gain a quantitative understanding of the landscape under historical reference period conditions and alternative future scenarios. Kevin is eager to bring his substantial experience, knowledge, and prolific set of tools and software to enhance Land Tender and the future suite of decision support applications that are currently being developed by Vibrant Planet.
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.
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.
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
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
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.
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).
“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).
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
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.
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.
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.
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.
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!
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.
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.