United States 

Department of 

Agriculture

Forest Service

Pacific Southwest 

Research Station

General Technical 

Report

PSW-GTR-232

October 2010

Using Forest Inventory 

and Analysis Data and the 

Forest Vegetation Simulator 

to Predict and Monitor 

Fisher (Martes pennanti) 

Resting Habitat Suitability

William J. Zielinski, Andrew N. Gray, Jeffrey R. Dunk, 

Joseph W. Sherlock, and Gary E. Dixon
The Forest Service of the U.S. Department of Agriculture is dedicated to the principle of 

multiple use management of the Nation’s forest resources for sustained yields of wood, 

water, forage, wildlife, and recreation. Through forestry research, cooperation with the 

States and private forest owners, and management of the National Forests and National 

Grasslands, it strives—as directed by Congress—to provide increasingly greater service to a 

growing Nation.

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Authors

William J. Zielinski is a research ecologist, U.S. Department of Agriculture, 

Forest Service, Pacific Southwest Research Station, 1700 Bayview Dr., 

Arcata, CA 95521; Andrew N. Gray is a research forester, U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research Station, Corvallis, 

OR 97331; Jeffrey R. Dunk is lecturer, Department of Environmental Science 

and Management, Humboldt State University, Arcata, CA 95521; Joseph W. 

Sherlock is a silviculturist, U.S. Department of Agriculture, Forest Service, 

Pacific Southwest Region, Vallejo, CA 94592; Gary E. Dixon is a forester 

(retired), U.S. Department of Agriculture, Forest Service, Forest Management 

Service Center, Fort Collins, CO 80526.

Cover photo by Michael Schwarz, Rocky Mountain Research Station.
Abstract

Zielinski, William J., Gray, Andrew N.; Dunk, Jeffrey R.; Sherlock, 

Joseph W.; Dixon, Gary E. 2010. Using forest inventory and analysis data 

and the forest vegetation simulator to predict and monitor fisher (Martes 

pennanti) resting habitat suitability. Gen. Tech. Rep. PSW-GTR-232. Albany, 

CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest 

Research Station. 31 p. 

New knowledge from wildlife-habitat relationship models is often difficult 

to implement in a management context. This can occur because researchers 

do not always consider whether managers have access to information about 

environmental covariates that permit the models to be applied. Moreover, 

ecosystem management requires knowledge about the condition of habitats over 

large geographic regions, whereas most research projects have limited spatial 

inference. For example, research has revealed much about the habitat of fishers 

(Martes pennanti) at various research sites in California, yet this work has not 

been translated into practical tools that managers can use to monitor fisher habitat 

regionally, or to evaluate and mitigate the effects of proposed forest management 

on fisher habitat. This led us to create new habitat models that are intimately 

linked to agency approaches to forest monitoring and software tools used by forest 

managers to plan timber harvests and vegetation management. We created habitat 

models that were integrated with these approaches and tools that forest managers 

use for two purposes: to inventory forest resources (i.e., Forest Inventory and 

Analysis [FIA] plots) and to simulate the response of stands to harvest, fire, 

insects, disease, and other disturbances (i.e., Forest Vegetation Simulator [FVS]). 

In this paper we provide an example of how to assess and monitor wildlife habitat 

using FIA vegetation monitoring protocols. We also provide an example of how to 

integrate an existing FIA-based model of fisher resting habitat into FVS, software 

that simulates the effect of alternative silvicultural treatments on vegetation data 

collected from field plots. Using these tools we produce quantitative predictions 

of the status of resting habitat quality for fishers, and describe how it can be 

monitored over time. We also provide an example of the effect of vegetation 

treatments on predicted fisher resting habitat, which illustrates a process that can 

be used to understand, reduce, or mitigate the effects of these activities on fisher 

habitat. This work on the fisher provides one example of how habitat assessments 

for wildlife could be advanced if they were developed with management 

applicability and implementation success as a goal. 

Keywords: Fisher, Martes pennanti, inventory, habitat, modeling, simulation.

1

Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

Introduction

The conservation of imperiled species requires monitoring of their populations 

and the abundance and distribution of their habitats (Nichols and Williams 2006, 

Yoccoz et al. 2001). Monitoring changes in habitat suitability is particularly 

challenging for species that are inconspicuous and occur over large regions 

(e.g., Huff et al. 2006). Frequently, land managers need to adapt the results of 

small-scale autecological research studies on a target species for use in rendering 

decisions about the effects of some management activity on a species’ habitat. 

This is a cumbersome process and one fraught with interpretation issues. Research 

projects that are relevant to habitat management decisions typically produce 

some form of predictive habitat model. These models are built by relating animal 

location to vegetation and topographic covariates to estimate the distribution and 

value of habitat within a study area. Researchers, however, often conduct their 

investigations in relatively small areas over short timeframes, and managers are 

tempted to extrapolate the findings outside the bounds of reasonable inference. 

Moreover, smaller scale study areas are often chosen because they contain high 

densities of the organism of interest but may not be representative of the variety of 

conditions to which the species is exposed. These problems can lead to errors in 

application. Perhaps more important, the researchers rarely use methods that are 

easily implemented within the forest planning platforms used by managers and 

decisionmakers. This is because the researcher’s choice of predictor variables (the 

environmental covariates) is usually based more on what they think will affect 

habitat choice by the target species than by what variables are available to forest 

managers for use in applying the model. The researcher often creates a studyspecific habitat sampling protocol (e.g., Zielinski et al. 2004) that does not lend 

itself to application or adoption by those responsible for forest management and 

species conservation. Moreover, the research investigation is based on assessing 

the status of environmental features within a single period. When the environment 

changes, owing to natural or human disturbances, there is no opportunity to update 

a model’s predictions because it requires collecting new field data in the treated 

area. 

Some research approaches, however, are less vulnerable to these shortcomings. 

It is possible, for example, to monitor categorical classes of wildlife habitat, and 

to evaluate the effects of silvicultural treatment on these habitats, when habitat is 

grossly defined as a set of particular vegetation or land-cover types (e.g., oak woodlands versus mixed-conifer forests) and is measured remotely via aerial photography or satellite imagery (e.g., Larson et al. 2004, McDermid et al. 2009, Vallecillo 

et al. 2009). These habitat relations, however, are crude, and such measures are 
2

GENERAL TECHNICAL REPORT PSW-GTR-232

sensitive only to major changes in vegetation type over time and are insensitive to 

the more subtle changes in vegetation structure and composition caused by thinning, small group selections, partial harvests, uneven management in general, and 

prescribed fire. In addition, the categorical habitat approach is often not repeatable 

and only focuses on landscape-level predictors, excluding important information 

that occurs at other scales of habitat suitability. An improved approach is to spatially model vegetation structure using forest inventory data and to classify wildlife 

habitat conditions in a geographic information system (GIS) (e.g., McDermid et al. 

2009, Spies et al. 2007). 

More difficult to assess are changes to the important, but localized, habitat 

elements such as nest sites or resting or roosting sites (Huff et al. 2006). These 

microhabitat features can be essential, but are exceedingly difficult (and expensive) 

to assess and monitor over large regions. Traditional research products, including several that the senior author (WZ) has co-authored (e.g., Slauson et al. 2007, 

Zielinski et al. 2004), also fail to provide managers with easily used quantitative 

tools to predict how alternative land management practices will affect the habitat 

values that are influenced by these microhabitat features. However, new initiatives 

are beginning to exploit the availability of government agency vegetation databases 

as the basis for predictive models (e.g., Dunk and Hawley 2009, Dunk et al. 2004, 

Fearer et al. 2007, Huff et al. 2006, Welsh et al. 2006). 

We provide in this report an example of how wildlife researchers can produce 

results that can be fully integrated with (1) management agency programs that 

monitor vegetation status and (2) the existing software that simulates the effects 

of management alternatives on forest structure and composition. Using the fisher 

(Martes pennanti) in the southern Sierra Nevada as our example, we demonstrate a 

research program that is co-developed with the Forest Inventory and Analysis (FIA) 

forest inventory program (Bechtold and Patterson 2004) and the Forest Vegetation 

Simulator (FVS) (Dixon 2002, Wycoff et al. 1982) such that foresters, biologists, 

and planners can use routinely collected FIA data and FVS to assess the status 

of predicted fisher resting habitat and to evaluate the effects of alternative forest 

management scenarios on future resting habitat value. We provide examples of two 

applications: (1) monitoring the status and change of fisher resting habitat on public 

and private lands in the southern Sierra Nevada and (2) simulating the effects of 

alternative silvicultural treatments on fisher resting habitat in the Sierra National 

Forest in California. Our work complements the important related work conducted 

by Huff et al. (2006) where FIA/Continuous Vegetation Survey (CVS) inventory 

plots were used to estimate nesting habitat for marbled murrelets (Brachyramphus 

marmoratus) in the Pacific States. Their work provides the best example, to date, 
3

Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

of developing a habitat model using institutional plot-based vegetation data to 

estimate microhabitat features over large regions.

Our approach, and that of other researchers beginning to appreciate this 

method, differs philosophically from the typical approach, whereby a researcher 

convinces the forest management and planning officials to adapt the reseacher’s 

models and to measure the predictor variables originally identified by them. 

Instead, we yield to the greater potential of developing the research, from the 

ground up, using sources of vegetation data that are regularly collected for inventory purposes and that can be integrated into software programs regularly used by 

foresters and silviculturists to predict forest change. 

Methods

Developing the FIA-Based Fisher Resting Habitat Model

The foundation for this work is a predictive resting habitat model for fishers in the 

southern Sierra Nevada that uses variables from the FIA plot sampling protocol as 

predictors. This work has been described previously (Zielinski et al. 2006) but will 

be briefly reiterated here and placed in context of the conceptual approach (fig. 1). 

We developed a predictive resting habitat model by comparing vegetation and topographic data at 75 randomly selected resting structures with 232 forest inventory 

plots from the FIA system in the southern Sierra Nevada. Fisher resting structures 

are important habitat features that are typically cavities in large-diameter trees and 

snags where a fisher seeks refuge during periodic resting bouts (e.g., Zielinski et al. 

2004). Resting structures were located during the course of two studies on the resting habitat ecology of fishers in the Sierra Nevada. The first was conducted from 

1994 to 1996 in the Sequoia National Forest in Tulare County (Zielinski et al. 2004) 

and the second from 1999 to 2000 in the Sierra National Forest, Fresno County, 

California (Mazzoni 2002). Animals were captured, fitted with radio-transmitter 

collars and tracked to their resting locations.

Vegetation attributes at fisher resting locations were measured using the FIA 

vegetation sampling protocol (Christensen et al. 2008, USDA Forest Service 2007) 

by centering the FIA plot on the resting structure. The FIA protocol involves the 

collection of vegetation data at four or five (see details regarding this variation on 

p. 5) subplots within a 1.0-ha circular footprint. Within each subplot, a nationally 

standardized set of attributes are measured or estimated, including live and dead 

trees, site productivity and topography, stand structure, and disturbance history. 

In addition, regionally important measurements are taken, including understory 

vegetation composition, the quantity of downed wood and litter, ground cover, and 

other physical features (see Christensen et al. 2008, USDA Forest Service 2007, 
4

GENERAL TECHNICAL REPORT PSW-GTR-232

Monitoring Comparison by

land allocation

Applications

Estimating future effects

of land management activities

THE FIA-BASED FISHER

RESTING HABITAT MODEL

A predictive model that uses variables

directly from the FIA field plot

sampling protocol as covariates.

Estimating mean (SE) predicted

fisher resting habitat from

FIA plots at TIME = 0

Comparison of mean predicted

fisher resting habitat

* Private vs. public lands

* Different public land types

* Among national forests

Step 1: Compare the FVS and

the FIA predictions to make

sure they are reasonable.

Step 2: Create a “fisher”

module in FVS.

Step 3: Invoke FVS as a 

platform for estimating effects

of alternative forest

management scenarios on 

predicted future fisher

resting habitat value.

..........at TIME = 1

..........at TIME = 2

..........at TIME = N

Time

0.25

0.20

0.15

0.10

0.05

0

0 1 2 ...... N

Figure 1–Conceptual diagram representing potential applications of a fisher resting habitat model linked to the Forest Inventory 

and Analysis (FIA) and Forest Vegetation Simulator (FVS). 
5

Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

http://fia.fs.fed.us and http://www.fs.fed.us/pnw/fia/publications/fieldmanuals.shtml 

for details on national and regional FIA sampling protocols and data availability). 

A predictive resting habitat model was developed by comparing characteristics 

at the 75 plots centered on the resting structures with the characteristics of the 

regularly sampled FIA points in the area. We limited the latter to include only 

those plots within the approximately 2230-km2 area within the range of suitable 

elevations (1100 and 2300 m) (Zielinski et al. 1997) that also were within either 

the Sierra Nevada or Sierra Nevada Foothills ecosection of the Ecological Unit of 

California system (Bailey 1994). To model the distinction between resting plots and 

available FIA inventory plots, we used nonparametric1 logistic regression, specifically, generalized additive regression models (GAMs) (Hastie and Tibshirani 1990), 

to evaluate a number of biologically feasible univariate and multivariate models 

(Burnham and Anderson 2002). The best model captured most of the variation and 

included the following variables derived from FIA plot information: overstory tree 

canopy cover (CC), basal area of trees < 51 cm diameter breast height (dbh) (BA_S), 

average hardwood dbh (DBH_HWD), maximum tree dbh (DBH_MAX), percentage slope (SLOPE), and the dbh of the largest conifer snag (CONSNG) (Zielinski 

et al. 2006). Based on the functional forms of the partial response curves of each 

predictor, we also estimated a parametric form of the best model (Zielinski et al. 

2006). The nonparametric version (GAM) was used in the monitoring application 

and the parametric version was used in all FVS applications.

Preliminary analyses: addressing changes in the FIA protocol—

The original FIA-based fisher resting habitat model (Zielinski et al. 2006) was 

developed using a regional FIA field protocol that was revised shortly thereafter 

with the adoption of new national standards. The original protocol was used in 

the 1990s and included five subplots and variable-radius sampling of trees. The 

FIA Program referred to it then as the “Region 5 [California] periodic inventory.” 

The revised (current) protocol is referred to as the “FIA annual inventory” and 

includes four subplots and nested fixed-radius subplot sampling. The new protocol 

is nationally consistent and viewed as a more efficient means for monitoring tree 

growth and mortality over time. Because the stand-level variables in the fisher 

model are aggregated at the plot level (i.e., subplot values are combined for each 

estimate), they should not be highly sensitive to plot design. Nonetheless, a primary 

concern regarding implementing the model, and using it for future monitoring of 

fisher habitat, was whether the variables in the model were estimated similarly 

1

 Parametric models assume that the variables can be characterized by some type of a 

probability distribution, whereas nonparametric models do not rely on assumptions that 

the data are drawn from a given probability distribution.
6

GENERAL TECHNICAL REPORT PSW-GTR-232

using the old and new protocol. To explore the extent of this variation, and to 

reassure us that the change in protocol would not be a problem, we selected 69 fully 

forested FIA plots on the Sequoia and Sierra National Forests between 1100 and 

2300 m elevation that had been sampled using both protocols and had experienced 

no substantial disturbance (e.g., fire, timber harvest, avalanche). The majority of 

these plots were remeasured within 4 or 5 years so that the primary difference 

in the values for the vegetation variables, and in predicted fisher resting habitat 

value, was the difference in the sampling protocols. We used correlation analysis to 

evaluate the differences in estimated values of predictor variables and in the habitat 

values predicted by the model for the two sampling schemes. We expected some 

differences among measurements at each plot owing simply to the spatial variation 

in tree density and the different locations of the subplots; means and distributions 

of predictor values were used to indicate whether the change in plot design would 

affect the fisher model predictions. 

Preliminary analyses: estimating the predicted resting habitat values in FVS—

The Forest Vegetation Simulator is a forest growth and yield forecasting system 

(Dixon 2002). It is an individual-tree forest growth model that has become the 

standard forest growth model used by forest industry and various state and federal 

government agencies, including the USDA Forest Service, USDI Bureau of Land 

Management, and USDI Bureau of Indian Affairs. Forestry professionals use FVS 

to develop silvicultural prescriptions, evaluate management scenarios, update 

inventory information, and provide input into forest planning models. Additional 

capabilities include forecasting vegetation structure, analyzing fire hazard, determining forest health risk, monitoring ecological processes, and carbon accounting 

(Dixon 2002). 

It was necessary to reproduce, in FVS, the predicted fisher resting habitat 

values that were generated from the FIA plot data. Although the original fisher 

model was a nonparametric GAM, we also reported a parametric version of the best 

predictive model (Zielinski et al. 2006). We reasoned that providing a parametric 

version would make the model easier to apply, as it was a simple algebric expression, compared to the necessity of applying statistical software and loess smoothing 

functions, which are necessary using the GAM model to generate predictions with 

new plot data. The parametric version was created by evaluating the shapes of the 

response curves of each of the variables and substituting an approximate parametric 

form (e.g., linear, polynomial, logarithmic). Slopes and intercepts for the parametric 

function were estimated using general linear models. This version proved far easier 

to implement in FVS than the original nonparametric version and was used for all 

FVS applications.
7

Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

Implementation of the parametric version of the fisher model in FVS was, 

however, not straightforward because of the challenge of replicating in FVS the 

values for some of the variables in the model. This process was simple for four of 

the variables, which were easily obtainable in FVS: basal area of trees < 51 cm 

dbh, average hardwood dbh, maximum tree dbh, and percentage slope. However, 

special effort was required to generate in FVS the values for percentage canopy 

cover and dbh of the largest conifer snag. 

Calculation of tree canopy cover for the fisher model and in FVS used the 

crown width equations of Warbington and Levitan (1992), which use tree species 

and dbh as inputs. To be clear, we use canopy cover, the proportion of the forest 

floor covered by the vertical projection of tree crowns, and not canopy closure, 

the proportion of the sky hemisphere obscured by vegetation when viewed from a 

single point (Jennings et al. 1999). The original fisher model only included trees of 

predominant, dominant, and codominant crown positions and at least 30 percent 

crown ratio in the estimate of dominant tree canopy cover. In FVS, however, 

crown position is not an input variable, so the problem is estimating which trees 

should be included in the estimate of canopy cover. After trying various classification rules, we decided to use 50 percent of the height of the 90th-percentile 

tree in the height distribution on the plot as the lower cutoff point (which we 

referred to as the “50–90 rule”). By using the height of the 90th-percentile tree, 

we avoided biases caused by the predominant trees on some plots. We could not 

simply modify FVS to allow crown position as an input variable because, although 

this crown position would be true at the time the plot was originally sampled, 

we would not have an algorithm to model its change over time as we simulated 

future disturbances such as fire and silvicultural activities. Therefore, providing 

for crown position upon input is not enough; rules and methods to dynamically 

change a tree’s crown position over time as other stand dynamics are happening 

would also be required. As an alternative, we used the “50–90 rule” to estimate 

which trees fall into the class from which canopy cover is estimated. Trees that 

met this standard, and which also were > 1 inch (2.54 cm) dbh and > 30 percent 

crown ratio, were included in the calculation. 

We used the Fire and Fuels Extension (FFE) (Reinhardt et al. 2007) to track 

snags in FVS, including the dbh of the largest conifer snag, a variable in the 

model. The FFE does not track each snag separately, but tracks them in 2-in 

(5.08-cm) dbh classes. There are eighteen 2-in classes that track snags up to 36 in 

(91.4 cm) dbh; snags larger than 36 in become members of the 19th class. However, 

the FFE tracks heights of snags entering each class, and if the height difference is 

greater than 20 ft (6.1 m), then the class gets split into two classes. The dbh of the 
8

GENERAL TECHNICAL REPORT PSW-GTR-232

largest conifer snag was estimated using the average dbh of the largest dbh class on 

plots that included a conifer snag. 

To explore the magnitude of difference that occurs in the variables and the 

model output when using either the original FIA process for estimation or the 

process as replicated in FVS, we selected data from five FIA plots in the southern 

Sierra Nevada. We used these data to compare the original FIA estimated value 

with the FVS estimated value for each of the six predictors in the fisher model. We 

also compared the model output (predicted fisher resting habitat value) that was 

produced when (1) the original FIA method for estimating the variables was used 

versus (2) the FVS version of the variables was used. We sought to confirm, in 

particular, that our approximations for canopy cover and largest conifer snag would 

result in no substantial difference in the predicted resting habitat value when represented in FVS, compared with the original predicted resting habitat values in FIA. 

Monitoring Status and Change of Predicted Resting Habitat 

We used the GAM version of the best predictive model (Zielinski et al. 2006) to 

estimate relative resting habitat suitability at each of the FIA plots that fell within 

the 1100 and 2300 m elevation range within and near the four national forests in 

the southern Sierra Nevada: Eldorado, Stanislaus, Sierra, and Sequoia. Values of 

each of the six predictor variables in the best model were derived from the data at 

each plot and put into the GAM model to generate a predicted probability of resting 

habitat value that ranged from 0 to 1. We illustrate the monitoring value of these 

data by comparing the mean predicted values from plots that were sampled in the 

mid-1990s (the Region 5 periodic inventory) with those sampled in the mid-2000s 

(the FIA annual inventory). For the purposes of this example, we refer to the 

first period as “1997,” the midpoint of the period 1995–1999 when the plot data 

were collected. The FIA annual inventory, which started in 2001 in California, is 

designed to sample 10 percent of the total plots each year, such that a monitoring 

cycle is concluded in 10 years. Thus, we refer to the midpoint of the second period 

(2001–2007) as “2004.” Because the Region 5 periodic inventory implemented on 

national forests in California in the 1990s intensified the density of plots in strata 

defined from vegetation maps, and the annual inventory is based on a national 

fixed-probability grid of one plot per 6,000 acres (2430 ha), the number of FIA 

plots that were sampled in 1997 was substantially greater (n = 626) than in 2004 

(n = 283). 
9

Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

Simulating the Effects of Silvicultural Alternatives Using FVS

An important feature of FVS is the event monitor, which allows the user to conditionally schedule management activities such as harvests and natural disturbances, 

based on a set of conditions that must occur or thresholds that must be reached. The 

event monitor contains predefined variables but also allows the creation of custom 

variables. Some predefined variables are tied to specific FVS extensions. The predicted fisher resting habitat value is estimated via a predefined FFE event monitor 

variable, which we call FISHERIN (calculated using the variables and coefficients 

in the parametric version of the model [Zielinski et. al. 2006]). The initial step 

involves activating the FFE (using the keywords FMIN and END), so that snag 

information is being tracked. The FVS can then calculate the predicted probability 

of resting habitat value, both before and after each harvest, vegetation treatment, or 

disturbance using a COMPUTE – END event monitor sequence.

As a demonstration of how one would use FVS to evaluate the effects of various treatments on predicted fisher resting value, we chose data from nine plots in 

five forest stands in the Sierra National Forest. These data were collected in support 

of an unrelated project, utilizing typical stand exam procedures. Plots were chosen 

with the goal of including a range of likely predicted fisher resting habitat values 

such that there were three plots each with low, medium, and high predicted fisher 

resting habitat value. For these plots, the value for the slope variable was derived 

from the stand the plot occurred in, and was not measured at the level of the plot. 

Each plot was subjected to a control (untreated but allowing for growth) and three 

simulated treatments in FVS: thin from below with a 12-in (30.5-cm) maximum 

diameter limit and a minimum of 60 percent canopy cover retained (12-in & 60%), 

thin from below with a 30-in (76.2-cm) maximum dbh limit and a minimum of 

50 percent canopy cover retained (30-in & 50%), and thin from below with a 

40-in (101.6-cm) maximum diameter limit and a minimum of 35 percent canopy 

cover retained (40-in & 35%). Trees that were < 4 in (10.2 cm) dbh at the time of 

simulated harvest were not removed, because there is typically no market for them. 

The three thinning scenarios, varying along a harvest tree size and canopy cover 

continuum, were applied (via simulation) to the plots in their current (2009) status. 

Predicted fisher resting habitat value was estimated in 2009, the simulated treatments occurred, and resting habitat value was estimated again at 5-year intervals 

into the future: 2014, 2019, and 2024. We predicted fisher resting habitat value for 

each year represented by the mean and standard error, calculated for the three plots 

in each initial predicted category (low, moderate, and high). Stand visualization 
10

GENERAL TECHNICAL REPORT PSW-GTR-232

software (SVS) (McGaughey 1997; http:forsys.cfr.washington.edu/svs.html) was used 

to portray the composition of each plot at each time step. The simulations we chose 

were not intended to evaluate specific prescriptions that were being considered by 

forest managers on the Sierra National Forest; rather they were simply an example 

of the range of typical prescriptions that are available to managers of mixed-conifer 

forests on the west side of the Sierra Nevada.

Results

Preliminary Analyses: Addressing Changes in the FIA Protocol 

The plot-aggregated values of the predictor variables were strongly correlated 

when comparing the old protocol and the new protocol (r = 0.65 to 0.89), except for 

dominant canopy cover (r = 0.32). The predicted fisher resting habitat value, when 

calculated based on variables collected by the old and new FIA protocol, was more 

strongly correlated using the parametric version of the model (r = 0.66) than the 

nonparametric version (r = 0.56). Some differences in values from the same plot were 

expected because the subplots of each design were located in different places, some 

different trees were sampled, and some samples were collected several years apart. 

Sierran forests may be particularly patchy (more spatially variable) compared to other 

forests (e.g., North et al. 2004), resulting in greater differences between plot designs 

than might be expected in other forests. Nevertheless, the similarity in the means 

and standard deviations for most attributes and the resting site probabilities (table 1) 

suggests there were no substantial effects of plot design on the distribution of values 

calculated. The exception was the calculation of dominant canopy cover, which may 

have resulted from differences in how crews were instructed to code tree dominance 

(e.g., by layer height vs. by light exposure). Nonetheless, the similarity in mean 

predicted fisher resting site probability among plot protocols for the nonparametric 

model (0.156 vs. 0.165) suggests the effect of differences in canopy cover calculation 

was not substantial. 

Preliminary Analyses: Estimating the Predicted Resting 

Habitat Variables in FVS

The differences between the estimates for each variable calculated from the FIA 

data versus those generated in FVS were generally small, are distributed about 

zero, and average very close to zero (table 2). The FVS approximation for percentage canopy cover is the most error-prone of the variables (table 2). Although the 

calculated values for predicted resting habitat may not be identical when compared 

to those generated from the FIA data, they are close and useful as an index of the 

predicted value for the purpose of comparing management alternatives. 
11

Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

Monitoring Status and Change of Predicted Resting Habitat

The means (and standard deviations) of predicted fisher resting habitat value for 

1997 and 2004 were 0.146 (0.008) and 0.135 (0.010), respectively (fig. 2; “All four 

national forests”). Estimated values for individual national forests (NF) differed in 

the direction of change between the 1997 and 2004 samples (fig. 2). Disturbance 

codes recorded on the plots suggest that recent harvest or fire on 20 percent of the 

plots measured on Eldorado NF may be responsible for the decline in predicted 

habitat, caused by declines in basal area of trees < 51 cm dbh, canopy cover, dbh of 

the largest conifer snag, and average hardwood dbh. Despite recent fire recorded on 

17 percent of the Sequoia NF plots, predicted habitat increased, apparently caused 

by much higher canopy cover values recorded in 2004. Predicted values on private 

Table 1—Mean and standard deviation of predictor variables and parametric and nonparametric 

fisher resting site probabilities for 69 undisturbed, fully forested plots in the study area measured with the periodic protocol and remeasured with the annual protocol (see text for protocol 

differences) within 2 to 8 years (mean = 4.7 years)

Periodic Annual

Mean Standard deviation Mean Standard deviation

DBH_MAX 104.7 43.9 102.6 42.8

BA_S 14.0 9.9 14.8 9.3

SLOPE 37.7 17.6 36.2 16.2

CC 41.5 21.3 49.5 25.7

CONSNG 75.9 51.4 72.1 54.5

DBH_HWD 10.6 13.4 9.0 11.4

Predicted resting habitat value:

Parametric 0.147 0.143 0.158 0.167

Nonparametric 0.156 0.187 0.165 0.205

DBH_MAX = maximum tree diameter at breast height (dbh), BA_S = basal area of trees ≤ 51 cm dbh, SLOPE = percentage of slope, 

CC = overstory tree canopy cover, CONSNG = dbh of largest conifer snag, DBH_HWD = average hardwood dbh.

Table 2—Values of the predictors from the fisher resting habitat model from five plots in the Sierra Nevada, 

calculated either directly from the Forest Inventory and Analysis (FIA) data or calculated using the Forest 

Vegetation Simulator (FVS) software

Predicted resting

DBH_MAX BA_S SLOPE CC CONSNG DBH_HWD habitat value

Plot FIA FVS FIA FVS FIA FVS FIA FVS FIA FVS FIA FVS FIA FVS Difference

5630 58.42 58.42 3.94 3.94 48.75 48.75 29.94 33.08 0 0 20.72 20.72 0.010 0.008 0.0018

6312 67.82 67.82 3.00 3.00 33.75 33.75 41.93 41.93 90.93 90.93 3.92 3.92 0.017 0.011 0.0055

5534 98.04 98.04 21.15 21.15 26.25 26.25 97.56 159.72 85.34 85.34 13.11 13.11 0.288 0.377 0.0889

8994 125.22 125.22 18.49 18.49 45.00 45.00 130.11 185.78 21.59 21.59 7.23 7.23 0.409 0.473 0.0641

6786 139.45 139.45 16.34 16.34 38.75 38.75 83.73 55.13 131.83 125.60 41.87 41.87 0.585 0.435 0.1506

DBH_MAX = maximum tree diameter at breast height (dbh), BA_S = basal area of trees ≤ 51 cm dbh, SLOPE = percentage of slope, 

CC = overstory tree canopy cover, CONSNG = dbh of largest conifer snag, DBH_HWD = average hardwood dbh.

Source: Zielinski et al. (2006). 
12

GENERAL TECHNICAL REPORT PSW-GTR-232

lands were lower, in general, than on federal 

or state lands (mean [SE] 2007 predicted 

resting value private = 0.076 [0.02] and 

USFS land = 0.135 [0.011]). However, there 

is little private land within the specified 

elevations in the vicinity of the four national 

forests in the southern Sierra Nevada, resulting in fewer of the plots occurring on private 

lands (15.2 percent). This may account for 

the larger standard error (especially relative 

to the mean) of predicted resting habitat 

value on private lands. 

Simulating the Effects of 

Silvicultural Alternatives

In our example, FVS simulated the effect 

of treatments over 15 years at nine plots 

that differed considerably in their initial 

predicted value to fishers as resting habitat 

(fig. 3). Plots with the highest initial 

predicted suitability (bottom row, fig. 4) 

had greater canopy cover, greater variation 

in tree size, and more hardwood and large 

snag components than those with much 

lower predicted values (top row, fig. 4). Not 

surprisingly, the magnitude of treatment 

effects was greatest for plots that began with 

Figure 2—Estimates of predicted fisher resting habitat 

value at 1997 and 2004 for each of the four southern 

Sierra Nevada national forests and for all four collectively. 

Bars are standard errors. The years 2014, 2024, and 

beyond are included to illustrate the opportunities for 

monitoring trend in predicted resting habitat value when 

future vegetation data become available. Sample sizes (N) 

are as follows, for 1997 and 2004, respectively: All four 

national forests = 626, 283; Stanislaus National Forest 

= 139, 61; Sequoia National Forest = 196, 92; Eldorado 

National Forest = 128, 60; and Sierra National Forest = 

163, 70. 

0.20

0.15

0.10

0.05

0

1997 2004 2014 2024 2034 2044

All four national forests

0.20

0.15

0.10

0.05

0

1997 2004 2014 2024 2034 2044

Stanislaus National Forest

0.20

0.15

0.10

0.05

0

1997 2004 2014 2024 2034 2044

Sequoia National Forest

0.20

0.15

0.10

0.05

0

1997 2004 2014 2024 2034 2044

Eldorado National Forest

0.20

0.15

0.10

0.05

0

1997 2004 2014 2024 2034 2044

Sierra National Forest
13

Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

No

treatment 

12′′ & 60% 30′′ & 50% 40′′ & 35% No

treatment 

12′′ & 60% 30′′ & 50% 40′′ & 35% No

treatment 

12′′ & 60% 30′′ & 50% 40′′ & 35%

Predicted probability of fisher resting habitat

2009

2014

2019

2024

Low-predicted-value plots 

(265/279/289)

Medium-predicted-value plots 

(78/264/274)

High-predicted-value plots 

(268/979/982)

Figure 3—Estimates of predicted fisher resting habitat value on the Sierra National Forest as a function of a control and three treatments simulated in the Forest Vegetation Simulator: (1) thinning to a 12-in (30.5-cm) dbh maximum and down to 60 percent canopy 

cover (12-in & 60%), (2) thinning to a 30-in (76.2-cm) dbh maximum and down to 50 percent canopy cover (30-in & 50%), and (3) 

thinning to a 40-in (101.6-cm) dbh maximum and down to 35 percent canopy cover (40-in & 35%). FVS assessed the predicted resting 

habitat value in 2009 and at 5-year intervals until 2024. N = 3 replicate plots for each combination of treatment and class of initial 

predicted value (low, medium, and high); bars are standard errors. 

the highest predicted values (fig. 3). Plots with high initial value had more habitat 

value to lose when treatments removed larger trees and reduced residual canopy 

cover. Owing to the short timeframe of our evaluation, plots with low predicted 

initial value could only change moderately under even the most ideal growing 

conditions. The mean of the plots with the highest initial resting habitat values 

dropped proportionately when the simulated treatments removed trees of increasing 

minimum size and decreasing residual canopy cover. This decrease is conspicuous 

when the future status (in year 2024) of two of the plots with initially high predicted 

resting habitat value (plots 979 and 982) are represented using stand visualization 

imagery (fig. 5). 
14

GENERAL TECHNICAL REPORT PSW-GTR-232

In general, predicted resting habitat value decreased proportional to severity of 

thinning (and the decrease in canopy cover). The fact that control plots increased 

in predicted habitat value over time (fig. 3) suggested that the simulations were 

performing logically. Interestingly, high-value plots having the lightest thinning 

(12-in & 60%) returned to their pretreatment habitat value within the 15-year 

simulation horizon (fig. 3). 

High

#286 0.471 #979 0.492 #982 0.555

#78 0.108 #264 0.031 #274 0.083

Medium

#265 0.006

Low

#279 0.005 #289 0.007

Initial predicted resting habitat suitability

Characteristics at 2009

Plots and predicted suitability

Figure 4—Stand visualization system images of the current state (2009) of nine plots on the Sierra National Forest described in the 

Forest Vegetation Simulator. The plots were chosen to include three plots with low, medium, and high predicted resting habitat value. 

Predicted resting habitat value is noted in lower right corner of each image. 
15

Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

Figure 5—Stand visualization system images of the future simulated state (2024) of two plots 

(numbers 979 and 982) on the Sierra National Forest that started with relatively high predicted 

fisher resting habitat value. The 2024 condition is simulated for three conditions: (1) no treatment, (2) thinning to a 12-in (30.5-cm) dbh maximum and down to 60 percent canopy cover 

(12-in and 60%) and thinning to a 40-in (101.6-cm) dbh maximum and down to 35 percent 

canopy cover (40-in and 35%). The number of small trees that are present after the thinning 

treatments represent those that were smaller than the 4-in (10.2-cm) minimum 15 years earlier, 

at the time of simulated harvest (see “Methods”). Predicted resting habitat value is noted in 

lower right corner of each image. 

#979 #982

0.673 0.650

0.482 0.650

0.220 0.354

40-in & 35% 12-in & 60% No treatment

Predicted suitability
16

GENERAL TECHNICAL REPORT PSW-GTR-232

Discussion

We demonstrated that an empirically derived habitat model, created using the FIA 

vegetation sampling protocol and implemented in FVS, is capable of producing 

quantitative measures of the status and trend in predicted fisher resting habitat 

value and is useful for evaluating the effects of simulated vegetation treatments. 

This is a major step toward developing methods to analyze the effects of forest management and change on important microhabitat features for a sensitive 

forest-dependent species. Our approach can facilitate a number of quantitative 

comparisons (see fig. 1), including monitoring change in predicted value over 

time and comparing predicted value among lands under different ownership and 

management. We have also demonstrated that the FIA-based model performs well 

in FVS, which means that predicted fisher resting value can be yet another output 

from analyses that compare the effects of silvicultural treatments on vegetation 

structure and composition, timber volume, and the abundance of fuels, among other 

traditional outputs. Such comparisons also provide a more refined and quantitative approach to evaluating land management scenarios relative to their potential 

impacts on habitat of threatened, endangered, or sensitive species. 

Prior to the availability of this tool, most public land managers were using 

crude, expert-opinion-based systems, such as California Wildlife Habitat Relations (CWHR) system (Mayer and Laudenslayer 1988), to estimate the effects of 

various treatments on fisher habitat. When not using these very general models, 

land managers often applied the results of research on fishers conducted at nearby 

small study areas, typically at scales that are inappropriate. In other words, they 

did the best they could, given the quality and quantity of information available. 

An exception is a similarly quantitative tool, which we are developing and testing 

in cooperation with managers on the Sierra National Forest. This new project uses 

FVS to understand how the simulated results of landscape-level treatments differ 

from the characteristics of home ranges chosen by female fishers.2

 This work will 

provide, for the home range scale, a tool similar to that developed here at the scale 

of the resting site. 

The approach we used, of building models with FIA-based predictors, can 

be applied to other species provided that researchers plan, from the beginning 

of their work, to measure FIA-based variables at locations of importance to 

the species of interest. For example, we have used FIA plot data to predict the 

2

 Thompson, C.M.; Zielinski, W.J.; Purcell, K.L. The use of landscape trajectory analysis 

to evaluate management risks: a case study with the fisher in the Sierra National Forest. 

On file with: W.J. Zielinski, Arcata, CA.
17

Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

occurrence of terrestrial salamanders, mollusks, and rodents (Dunk and Hawley 

2009, Dunk et al. 2004, Welsh et al. 2006). For these small vertebrates and 

invertebrates, one can build a model to actually predict occurrence (or even 

abundance) of the species on existing plots within the FIA annual inventory system, 

provided that the plot can be sampled without disturbance. Although the models 

constructed for these small species were developed using FIA plot data, they have 

not been vetted through the FVS system and thus require more examination before 

they can be used for the various purposes proposed here for the fisher model (see 

fig. 1). It would not be difficult, however, to prepare them for this purpose.

The approach for species that use larger areas must be different. As 

demonstrated here for the fisher, and for other species that use areas that are 

much larger than the typical 1-ha FIA plot footprint, models must be constructed 

by installing new FIA plots centered on a habitat element of conservation 

importance (e.g., a resting, denning, roosting, or nesting site) or distributed at 

relatively high density within forest stands of particular importance to the species. 

For these larger species, the plots installed for this purpose are off the regular FIA 

grid and intended to be sampled only once, for the specific purpose of developing 

the predictive habitat model. As demonstrated here for the fisher, this model can 

then be used to predict habitat value at the routinely resampled FIA vegetation 

plots that fall within the region of inference each time they are resampled. A useful 

habitat model constructed in this fashion can be applied to subsequently sampled 

data at FIA plots in perpetuity, or until the relationship between the species and its 

habitat changes. If this happens, a new habitat relationship model, based on new 

empirical FIA-based field data, would need to be developed. 

The FIA-based wildlife habitat models can reveal important contrasts in the 

predicted value of lands managed for different purposes. For example, Huff et al. 

(2006) used FIA-based sampling to estimate the area of high suitability nesting 

habitat for marbled murrelets and contrasted values for each of the Pacific States. 

Dunk and Hawley (2009) used FIA-based sampling to evaluate habitat and reserve 

associations of the red tree vole (Arborimus longicaudus) throughout the majority of 

the species’ range. Subsequently, their model was used to evaluate the distribution 

of suitable tree vole habitat within various land ownership categories (using FIA 

data) within a smaller portion of the species’ range, in order to assist in the evaluation of a petition to list the red tree vole under the Endangered Species Act 

(J. Dunk. 2009. Personal communication, Department of Environmental Science 

and Management, Humboldt State University, Arcata, CA). As demonstrated here, 

predicted values from our model can be used in a similar fashion, to contrast 

resting habitat value among national forests and between public and private 
18

GENERAL TECHNICAL REPORT PSW-GTR-232

lands. Moreover, if we assumed a threshold predicted value, above which sites are 

assumed to be “resting habitat,” our model could also be used (sensu Huff et al. 

2006) to estimate the amount and distribution of resting habitat. 

Every predictive habitat model has spatial boundaries. The model we have 

developed applies only to the area over which we collected the resting site data from 

radio-collared fishers, which includes relevant elevations on the west slope of the 

Sierra and Sequoia National Forests. Applications for other portions of the fisher’s 

range will require the development of separate models. We are in the process of 

doing this to predict resting habitat in northwestern California, and we expect that 

the variables in the selected model will be quite different than those that appear in 

the model used here. This has already been confirmed in habitat selection work that 

was developed outside the FIA framework; variables that predicted fisher resting 

sites in the southern Sierra Nevada were different than those that predicted resting 

sites in northwestern California (Zielinski et al. 2004). Thus, we caution against 

applying the current model outside the southern Sierra Nevada. 

During the course of developing the model for the southern Sierra Nevada, 

we learned that we could probably improve future applications for fishers, and for 

other species, in terms of how two of the variables were estimated from FIA data 

or interpreted in FVS: canopy cover and slope. The derivation of canopy cover 

was indirect and awkward in FVS, requiring information from the FIA plot data 

about the dominance status (predominant, dominant, or codominant) and crown 

ratio, by species, for all trees > 1 in dbh. We believe that the estimation of canopy 

cover would be easier to generate, and less variable, if it excluded consideration of 

dominance status and was based instead simply on total tree cover. Percentage of 

slope, in developing the original fisher model, was estimated at each plot. However, 

when the model is applied to new plots, which may not have been measured using 

the FIA protocol, slope may not have been measured at the plot, only at the level of 

the stand. This was the situation for the test data evaluated in our examples (figs. 

3 through 5). When measured this way, all plots that occur within the same stand 

will have the same value for slope, regardless of the true slope at the plot. Given 

how influential the slope variable is in estimating the predicted resting habitat value 

(Zielinski et al. 2006), this can result in an application of the model that predicts 

resting habitat value with less precision. We suggest that as practitioners plan to use 

this model, they should use slope data measured specifically for each plot. 

Forest planning is an increasingly complex process in which the stakeholders 

demand a rigorous and scientific approach. Linking our description of fisher resting 

habitat to FIA and FVS fulfills the need for quantitative predictions of the effects 

of forest management scenarios. Land managers are not served well by the research 
19

Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

community when we do not provide tools that are useable to them. Without such 

tools, managers will continue to use literature reviews, results from small-scale 

studies, general habitat models (e.g., CWHR models; California Department of Fish 

and Game 1992), or their professional opinion to evaluate the effects of proposed 

management actions. The various publics that scrutinize land management planning 

documents are sophisticated and call for increasingly more exacting standards to 

evaluate the effects of proposed actions on species and their habitat. Our approach 

fulfills that need for analyses of fisher resting habitat that occur in the southern 

Sierra Nevada. 

Successful application of a wildlife habitat model that is integrated with 

institutional forest monitoring and prediction requires considerable effort. This 

work cannot be done by a wildlife biologist in isolation. It requires front-end 

co-development with specialists familiar with the inventory, monitoring, and 

forest simulation technologies that will be used to describe the effects on the target 

species. Research must be designed for application, in much the same way an 

experiment is designed, a priori, to be statistically valid. We describe here the 

process of translating research results into a quantitative tool for decisionmakers. 

The fisher provides one example of how habitat assessments for other species of 

wildlife could be advanced if they were developed together with managers, in a 

language familiar to managers, and with their implementation success as a goal. 

Acknowledgments

We thank T. Gaman of East-West Forestry for contributions to the development 

of the original fisher FIA model and for collecting FIA plot data necessary to 

construct the original model. R. Schlexer helped manage the FIA plot data. We 

are grateful to E. Smith-Mateja, C. Keyser, K. Purcell, and W. Spencer for review 

comments and R. Schlexer and L. Sullivan for editorial assistance. Funding for the 

development of the FIA-based fisher model was contributed by the Pacific Southwest Region of the USDA Forest Service and the Yreka office of the USDI Fish 

and Wildlife Service. 

English Equivalents

When you know: Multiply by: To find:

Centimeters (cm) 0.394 Inches (in)

Meters (m) 3.28 Feet (ft)

Kilometers (km) .621 Miles (mi)

Square kilometers (km2) .386 Square miles (mi2)

Hectares (ha) 2.47 Acres
20

GENERAL TECHNICAL REPORT PSW-GTR-232

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Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

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GENERAL TECHNICAL REPORT PSW-GTR-232

Appendix A

A-1: Example keyword file needed to run the Forest Vegetation Simulator. 

This hypothetical example shows the commands needed to simulate a thinning 

scenario. The existing stand is thinned to maintain approximately 60 percent of 

the stand basal area. The predicted fisher resting site value (hereafter the “Fisher 

index”) is tracked throughout the simulation. Lines beginning with the “*” 

character are treated as comment lines by FVS and are useful for documentation. 

Additional information available in Dixon 2003.

*----------

* This section of keywords gives control instructions to FVS.

* It's telling FVS what stand has been selected, the stand’s 

* inventory year, to run 6 projection cycles, with the first 

* cycle 7 years long rather than the default 10 years. The 

* database keywords tell FVS how to select the stand and 

* tree data from the ACCESS Database.

*----------

*

Screen

StandCN

647B

InvYear 2003

TimeInt 1 7

NumCycle 6

*

Database

DSNIn

snfplus04 _ 1inv.mdb

StandSQL

SELECT *

FROM FVS _ StandInit

WHERE Stand _ CN = '%Stand _ CN%'

EndSQL

TreeSQL

SELECT *

FROM FVS _ TreeInit

WHERE Stand _ CN = '%Stand _ CN%'

EndSQL

END

*----------

* This section of keywords instructs FVS to simulate a thinning.

* In this example, it only thins trees greater than 1” DBH and

* less than 25” DBH.

*----------
25

Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

*

ThinDBH 2010 1 25 1. All 0. 41

*

*----------

* This section of keywords initiates the Fire and Fuels extension of FVS

* which is needed to provide snag information for the calculation of the

* Fisher index. It also instructs FVS to compute two user-defined Fisher

* index values, BFI and AFI, defined as follows:

* BFI is the Fisher index value at the start of each projection cycle;

* AFI is the Fisher index value after a cutting occurs, and is computed

* whenever a thinning occurs in the simulation.

*---------- 

*

FMIN

END

*

COMPUTE 0

BFI = FISHERIN

END

IF

EVPHASE EQ 2 AND CUT EQ 1

THEN

COMPUTE

AFI = FISHERIN

END

ENDIF

*

*----------

*----------

* This section instructs FVS to send the computed variables (described above) 

* and the stand summary data to an EXCEL file. 

*----------

DataBase

DSNOut

FVSout.xls

Summary

Compute 0 0

End

*----------

* These are FVS control keywords telling the model to start the

* simulation and stop when it is done. FVS has the ability to simulate

* several management scenarios within one run sequence.

*----------

*

PROCESS

STOP
26

GENERAL TECHNICAL REPORT PSW-GTR-232

A-2: Example output showing how FVS can send the computed variables and stand 

summary statistics to an output format. As requested in the keyword file, the variable BFI (the Fisher Index value at the start of each projection cycle) is displayed 

for every projection cycle, and the variable AFI (the Fisher Index value after a 

treatment or disturbance occurs) is only displayed after a harvest cut in a projection 

cycle. a = example output in original database format; b = output in Excel format. 

a:

Id CaseID StandID Year BFI AFI

1 1 647B 2003 0.4832

2 1 647B 2010 0.5441 0.1232

3 1 647B 2020 0.1886

4 1 647B 2030 0.2685

5 1 647B 2040 0.2980

6 1 647B 2050 0.3555

Id CaseID StandID Year Age Tpa BA SDI

1 1 647B 2003 0 660 197 406

2 1 647B 2010 7 640 211 427

3 1 647B 2020 17 667 153 332

4 1 647B 2030 27 661 178 375

5 1 647B 2040 37 632 197 403

6 1 647B 2050 47 596 215 428

7 1 647B 2060 57 571 236 458
27

Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

b:
28

GENERAL TECHNICAL REPORT PSW-GTR-232

A-3: Fortran computer code listing for the FVS subroutine that computes the 

Fisher Index.

 ENTRY FISHER (FINDX)

C----------

C THIS ENTRY COMPUTES A VALUE FOR THE FISHER RESTING HABITAT

C SUITABILITY (THE “FISHER INDEX”)

C

C REF: ZIELINSKI, WILLIAM J.; TRUEX, RICHARD L.; DUNK, JEFFREY R.;

C GAMAN, TOM. 2006. USING FOREST INVENTORY DATA TO ASSESS FISHER

C RESTING HABITAT SUITABILITY IN CALIFORNIA. ECOLOGICAL

C APPLICATIONS 16(3). PP 1010-1025.

C-----------

C SEE IF WE NEED TO DO SOME DEBUG.

C-----------

 CALL DBCHK (DEBUG,'FISHER',6,ICYC)

 IF(DEBUG)WRITE(JOSTND,10)ICYC

 10 FORMAT(' ENTERING SUBROUTINE EVPDEF, ENTRY FISHER, CYCLE =',I4)

C----------

C INITIALIZE VARIABLES.

C----------

 TEMP = 0.

 FINDX = 0.

 BASM = 0.

 CCPCT = 0.

 ADHW = 0.

 DMAX = 0.

 DSNMAX = 0.

C----------

C IF FIRE MODEL IS NOT ACTIVE, FOR SNAG PROCESSING, RETURN A ZERO.

C----------

 CALL FMATV(LFIRE2)

 IF(.NOT. LFIRE2)GO TO 500

C----------

C THIS IS CURRENTLY ONLY ALLOWED IN THE CALIFORNIA VARIANTS AND

C ONLY FOR R5 FORESTS (I.E. BASED ON R5 CROWN WIDTH EQNS).

C---------- 

 CALL VARVER(VVER)

 IF(VVER(:2).EQ.'NC' .AND. IFOR.NE.4 .AND. IFOR.NE.5)THEN

 GO TO 20

 ELSEIF(VVER(:2).EQ.'CA' .AND. IFOR.LT.6)THEN

 GO TO 20

 ELSEIF(VVER(:2).EQ.'SO' .AND. IFOR.GT.3)THEN

 GO TO 20

 ELSEIF(VVER(:2).EQ.'WS')THEN

 GO TO 20

 ELSE

 GO TO 500

 ENDIF

 20 CONTINUE

 IF(ITRN .LE. 0) GO TO 500
29

Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

C----------

C CANOPY COVER PERCENT IS ONLY FOR PREDOMINANT, DOMINANT, AND 

CODOMINENT

C TREES. PROXY THIS BY TAKING ALL TREES WHICH ARE AT LEAST AS TALL 

AS

C 50% OF THE HEIGHT OF THE 90TH %-TILE TREE IN THE HEIGHT 

DISTRIBUTION

C

C EXCLUDE TREES WITH CROWN RATIO LESS THAN 30% FROM THE CALCULATION. 

C EXCLUDE TREES WITH DBH LT 1" FROM THE CALCULATION

C FIRST SORT BY HT. NEXT LOOP THROUGH THE TREES AND COMPUTE PERCENT

C CANOPY COVER.

C----------

 DO 25 I=1,MAXTRE

 IF(I .LE. ITRN)THEN

 INDEX(I)=I

 ELSE

 INDEX(I)=0

 ENDIF

 25 CONTINUE

 CALL RDPSRT(ITRN,HT,INDEX,.FALSE.)

 IF(DEBUG)WRITE(JOSTND,*)' INDEX = ',(INDEX(I),I=1,ITRN)

C

 SUMPIN = 0.

 HTMAX = 0.

 DO 30 I=1,ITRN

 ISRTI = INDEX(I)

 P = PROB(ISRTI)

 IF(DEBUG)WRITE(JOSTND,*)' IN FISHER CCPCT1, SUMPIN,ISRTI,P,HT,HTMA 

 &X,TPROB= ',SUMPIN,ISRTI,P,HT(ISRTI),HTMAX,TPROB

 IF(DBH(ISRTI) .LT. 1.) GOTO 30

 IF(ICR(ISRTI) .LT. 31) GOTO 30

 IF(HT(ISRTI) .GE. HTMAX)THEN

 CCPCT=CCPCT + P*CRWDTH(ISRTI)**2.0

 SUMPIN = SUMPIN + P

 IF(DEBUG)WRITE(JOSTND,*)' ISRTI,P,CRWDTH,CCPCT,SUMPIN= ',

 & ISRTI,P,CRWDTH(ISRTI),CCPCT,SUMPIN 

 ENDIF

 IF(SUMPIN.GT.TPROB*0.10 .AND. HTMAX.EQ.0.)HTMAX = HT(ISRTI)*.50

 30 CONTINUE

 CCPCT = 100.0*CCPCT*0.785398/43560.

C----------

C LOOP THROUGH TREES AND CALCULATE:

C 1) BASAL AREA IN SMALL TREES, 5-51 CENTIMETERS DBH 

C (BASM: SQUARE METERS/HA)

C 2) PERCENT CROWN COVER OF DOMINANT AND CODOMINANT TREES

C (CCPCT: PERCENT, ALLOW FOR OVERLAP)

C 3) ARITHMETIC AVERAGE DIAMETER OF ALL HARDWOODS

C (ADHW: CENTIMETERS)

C 4) DIAMETER OF THE LARGEST LIVE TREE IN THE STAND 

C (DMAX: CENTIMETERS)

C 5) DIAMETER OF THE LARGEST CONIFER SNAG IN THE STAND

C (DSNMAX: CENTIMETERS)

C----------
30

GENERAL TECHNICAL REPORT PSW-GTR-232

 DO 90 I=1,ITRN

 ISPC = ISP(I)

 D = DBH(I)

 P = PROB(I)

 IF(D .GT. DMAX)DMAX=D

C

C NOTE: IN THE DIAMETER SCREEN BELOW, WE ARE USING 5" AS THE LOWER 

C LIMIT INSTEAD OF 5 CM (1.9685") TO BE CONSISTENT WITH THE

C WAY ANDREW GRAY (PNW-FIA) SAYS IT NEEDS TO BE CALCULATED

C WHICH IS CONTRARY TO THE PUBLICATION. THIS MAY NEED TO BE

C CHANGED.

C IF(D.GE.1.9685 .AND. D.LT.20.0787)THEN

C

 IF(D.GE.5.0 .AND. D.LT.20.0787)THEN

 BASM = BASM + 0.0054542*P*D*D

 ENDIF

 IF(VVER(:2).EQ.'WS' .AND. (ISPC.EQ.7 .OR. ISPC.EQ.11))THEN

 ADHW=ADHW + D*P

 SUMTPA=SUMTPA + P

 ELSEIF(VVER(:2).EQ.'NC' .AND. (ISPC.EQ.5 .OR. ISPC.EQ.7 .OR.

 & ISPC.EQ.8 .OR. ISPC.EQ.11))THEN

 ADHW=ADHW + D*P

 SUMTPA=SUMTPA + P

 ELSEIF(VVER(:2).EQ.'CA' .AND. ISPC.GE.26)THEN

 ADHW=ADHW + D*P

 SUMTPA=SUMTPA + P

 ELSEIF(VVER(:2).EQ.'SO' .AND. ((ISPC.GE.21 .AND. ISPC.LE.31) .OR.

 & ISPC.EQ.33))THEN

 ADHW=ADHW + D*P

 SUMTPA=SUMTPA + P

 ENDIF

 90 CONTINUE

 IF(DEBUG)WRITE(JOSTND,*)' IN FISHER DMAX,ADHS,SUMTPA,BASM,CCPCT= '

 &,DMAX,ADHW,SUMTPA,BASM,CCPCT 

C----------

C CALCULATE:

C 5) DIAMETER OF THE LARGEST CONIFER SNAG IN THE STAND

C (DSNMAX: CENTIMETERS)

C----------

 CALL FMEVMSN(DSNMAX)

 IF(DEBUG)WRITE(JOSTND,*)' IN FISHER DSNMAX= ',DSNMAX

C----------

C CONVERT ENGLISH UNITS TO METRIC UNITS WHERE APPROPRIATE

C PERCENT CROWN COVER SHOULD BE EQUIVALENT CALCULATED EITHER WAY

C CALCULATE THE FISHER HABITAT SUITABILITY INDEX

C----------

 DMAX = DMAX*2.54

 DSNMAX = DSNMAX*2.54
31

Using FIA Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability

 IF(SUMTPA .GT. 0.)THEN

 ADHW = (ADHW/SUMTPA)*2.54

 ELSE

 ADHW = 0.

 ENDIF

 BASM = BASM*0.2295643

 IF(DEBUG)WRITE(JOSTND,*)' IN FISHER, CCPCT,BASM,ADHW,DMAX,SLOPE,',

 &'DSNMAX= ',CCPCT,BASM,ADHW,DMAX,SLOPE,DSNMAX 

C

 IF(CCPCT .GT. 0.)THEN

 X1=ALOG10(CCPCT)

 ELSE

 X1 = 0.

 ENDIF

 IF(BASM .GT. 0.)THEN

 X2=ALOG10(BASM)

 ELSE

 X2 = 0.

 ENDIF

 IF(ADHW .GT. 0.)THEN

 X3=ALOG10(ADHW)

 ELSE

 X3 = 0.

 ENDIF

 IF(DMAX .GT. 0.)THEN

 X4=ALOG10(DMAX)

 ELSE

 X4 = 0.

 ENDIF

 IF(SLOPE .GT. 0.)THEN

 X5=ALOG10(SLOPE*100.)

 ELSE

 X5 = 0.

 ENDIF

C

 TEMP = -22.1217941 + 2.461062*X1 + 2.15615937*X2 + 0.47133361*X3

 & + 4.55271635*X4 + 2.16130549*X5 + 0.00793579*DSNMAX

 FINDX=EXP(TEMP)/(1+EXP(TEMP))

C

 500 CONTINUE

 IF(DEBUG)WRITE(JOSTND,*)' LEAVING FISHER, TEMP,FINDX= ',TEMP,FINDX

 RETURN


U.S. Department of Agriculture

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P.O. Box 3890

Portland, OR 97208-3890

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Penalty for Private Use, $300