AN APPLICATION OF POINT-IN-TIME EXTENSIVE FOREST INVENTORY DATA ANALYSIS
TO ASSESSMENT OF SPRUCE BARK BEETLE INFESTATION TRENDS AND DYNAMICS
Willem W. S. van Hees
U.S. Forest Service, Pacific Northwest Research Station
Forestry Sciences Laboratory, Suite 200, 3301 C Street
Anchorage, AK 99503-3954
ABSTRACT: Estimations of areas of forestland and numbers of trees affected by
a spruce bark beetle infestation on Alaska's Kenai Peninsula are presented.
Selected forest stand conditions pertinent to infestation dynamics are
examined. Application to improving understanding of infestation trends and
dynamics while providing logistical efficiency is demonstrated.
INTRODUCTION
The purpose of this paper is to demonstrate that it is possible to use data
from an extensive forest inventory to assess some dynamics of a widespread
insect infestation. Extensive inventory, in this context, means two things --
large area coverage and low sampling intensity.
The point of this demonstration is that logistical and financial saving
might be realized when the needs driving traditional forest inventories are
combined with needs of regional land use planners to understand the current
state, and likely progress, of insect infestations. Often, multiple
observations in time must be procured in order to estimate conditions or
trends in dynamic systems such as insect infestations.
The forest inventory used here as an example was conducted on Alaska's
Kenai Peninsula, ( Figure 1) by the Anchorage Forestry Sciences Laboratory of
the US Forest Service's Pacific Northwest Research Station.
The inventory unit area is about 2.3 million acres (van Hees and Larson,
1992). Nearly 40 percent of this, almost 2 million acres, is forested. Not
all of this is productive forest land. Only 480 thousand acres are classed as
timberland. That is, forest land capable of producing 20 cubic feet of wood,
or more, per acre per year.
The white spruce component of the forest land on the Kenai Peninsula has
been subjected to an ongoing infestation by spruce bark beetles for a number
of decades. Rapid expansion of the infestation has recently occurred for
reasons including favorable weather conditions and an aging resource. Most
forest land on the Peninsula is not affected by the infestation; only
timberland, the more productive segment is so favored. The particular
infestation dynamics addressed in this paper are those relating to changes in
area of infestation, numbers and sizes of trees affected, and the relation
between tree stocking and recent radial growth in the face of the infestation.
METHODS AND DATA
The inventory design used on the Kenai Peninsula was a fairly standard
stratified two-phase design. In the first phase, about 5,600 photo points
were systematically distributed over 1:60,000-scale aerial photos and then
interpreted. Each photo point was classified into one of four land classes;
timberland, other forest land, nonforest, and water. From the photo points, a
random sample of about 1,200 points was selected for ground visitation.
Because the focus of the inventory was on timber, only timberland locations
and other forest land locations photo-interpreted as being marginally capable
of producing 20 cubic feet per acre per year were actually visited on the
ground. 129 plots were selected for ground visitation. The ground plots were
5-point, variable radius clusters which covered about 5 acres. Inventory
design error standards were plus or minus 3% per million acres of timberland
and plus or minus 10% per billion cubic feet of gross tree volume given 68%
confidence limits.
In order to develop an understanding of a dynamic system like an insect
infestation from a point-in-time observation such as a single forest
inventory, it is necessary that the observations of the system be separated
into classes or groups respresenting various stages of the system. The
analyses referred to in this paper require knowledge of the current stage of
infestation of the stands in which the ground plots were placed. To obtain
this, forested ground plots were separated into four infestation categories
(van Hees, 1992).
The separation was based on whether or not there were beetle attacked trees
on the plot, whether or not those trees were living or dead, and if dead, how
long. The stage-of-infestation categories were: uninfested, potential,
ongoing, and past.
Uninfested -- Plots where no live or dead tree showed any sign of
beetle attack.
Potential infestation -- Plots where only live trees showed signs of
beetle attack and plots where live trees and
trees dead more the 5 years showed signs of attack. This latter group
represents areas of potential re-infestation.
Ongoing infestation -- Plots where live trees and trees dead less than
5 years showed signs of beetle attack plus plots
where live trees, trees dead less than 5 years, and those dead more
than 5 years showed indication of attack.
Past infestation -- Plots where only dead trees showed signs of beetle
attack.
Estimates of the area of productive forestland affected by each infestation
category were developed by summing the area expansion factors for the plots
that fell in each stage-of-infestation category. As a way of checking whether
or not the inventory estimates were at all reflective of reality, an effort to
validate these estimates was make by comparing them with estimates derived
from successive annual aerial survey maps.
By "subtracting" the current annual aerial survey map from the prior one, a
"difference" map was produced that allowed development of estimates of the
area of new, or potential infestation. Maps of successive prior years were
overlaid to derive difference maps for the other infestation categories.
Comparing the inventory estimates with the mapped estimates ( Figure
2 )
shows that the inventory estimates are likely reasonable. Given the vagaries
of aerial mapping and sampling errors, the two sets of estimates agree well.
From these area estimates it is apparent that the infestation has expanded
and there are indications it will continue to expand. The evidence for
recent expansion is that the area of ongoing infestation is larger than the
area where the infestation has passed through.
Indication that the infestation could continue to expand is seen in the
magnitude of area with potential infestation in context with the area of past
infestation.
Trees unsuccessfully attacked in stands once attacked are more likely to
succumb in subsequent attacks because of probable lack of vigor that led to
the first attack and stress induced by the first attack. The existence of a
sizable, vulnerable resource (the area where infestation has passed through
already) along with an area of potential infestation of nearly equal size
points at an infestation that can at least maintain its current magnitude.
For the resource manager interested in quantities of timber resource
currently and potentially available for salvage, plot level information can
easily be used to estimate numbers of trees and volumes. The pieces of plot
level information referred to here are trees-per-acre and volumes-per-acre by
diameter class figures. Segregating this information by infestation category
can give the manager an idea of the resource already available and that which
will likely become available in the near future.
So, how much of the resource is involved? How much is currently alive, how
much is dead, and what part is likely to die in the near future?
This analysis started with a look at the overall situation. Nearly half of
the current total white spruce resource (including both living and dead trees)
is in beetle attacked areas ( Figure 3 ). And most of that half is in areas
where the infestation is ongoing.
The next largest component is in areas where the infestation has passed
through. This supports the notion that the infestation is expanding. Also,
this picture provides graphic evidence that the infestation has been around
for some time.
A clearer picture of the impact of the infestation is formed if percent of
total numbers of white spruce is examined ( Figure 4 ). In those areas where
the infestation has passed through and in those areas where it is ongoing, the
trees in almost every diameter class that have been attacked or are at risk of
attack represent between 15 and 30 percent of the total resource in that
diameter class.
An observation of real interest here is that for those areas where the
infestation is ongoing or potential, most of the diameter classes are
suffering equally within the category. Definite indication that all segments
of the white spruce resource are currently in trouble or will be so shortly.
The next questions then, are -- how much of the live resource is actually
under attack and how much of the dead resource is so because it was attacked?
Figure 5 shows numbers of just the live tree component separated by attack
status - that is under attack or not. The 'past' category has no live trees
under attack -- by definition. Because of the numbers of trees in the
uninfested category, the scale of this graphic tends to downplay the
importance of the number of live trees under attack.
By examining the percent of live trees in each category showing signs of
attack, the impact of the infestation becomes apparent ( Figure
6 ). Of the
live trees in areas where the infestation is ongoing, between 5 and 25 percent
of the trees in the affected diameter classes are under attack. In the areas
where there are potential infestations, between 2 and 12 percent of the trees
in the affected diameter classes are under attack.
Looking at the dead component ( Figure 7 ), it is noticeable that, except for
areas where there are potential infestations, most of the dead trees in each
diameter class are dead due to beetle attack. Again, looking at the same
information but in terms of percentages provides a dramatic graphic of the
impact of the infestation ( Figure 8 ). For most diameter classes, 60 percent
or more of the trees died because of beetle attack.
These snapshots of the past, current, and likely future condition of the
white spruce resource are only a sample. However, at this point a few
conclusions can be drawn. ONE -- It is apparent that the infestation has
increased substantially in recent years, and TWO -- it is likely the
infestation will maintain, if not increase, its magnitude for the near future.
The assertion that the infestation has increased derives from three
observations of the data:
1. When estimates of the percent of all dead trees affected by beetle
attack are compared for areas of ongoing versus areas of past
infestation it is noticeable that except for the 4- and 6-inch diameter
classes, most succeeding diameter classes show higher relative numbers died
due to beetle attack in the areas of ongoing infestation than in areas where
the infestation passed through. In other words, more of the resource is dead
because of ongoing infestations than because of past infestations.
2. As noted earlier, the estimated area of timberland affected by ongoing
infestations is high, 121 thousand areas. This is greater than the
estimated area where infestations have run their course; 85 thousand acres --
a definite indication of expansion.
3. Half the current white spruce resource is within beetle attack areas.
The possibility that the infestation would likely maintain and perhaps
increase in magnitude is based on two observations:
1. The residual, live-tree resource on areas where the infestation has
passed is larger than the live-tree resource in either areas of
ongoing infestation or areas of potential infestation. As mentioned above,
this is a vulnrable resource and will succumb easily if any subsequent attack
occurs.
2. The estimated area of potential infestation, 81 thousand acres, is
already 2/3 the area in currently ongoing infestations (121 thousand
acres). This suggests that at least for the near term (next 5 years), the
infestation can maintain its current magnitude.
Although most of the analyses presented so far refer to numbers of trees,
these could be easily converted to volume based analyses.
An understanding of how many acres, trees, or cubic feet of volume are
being subjected to infestation is useful for regional awareness of the extent
of the problem and for assessing the possible flow of expected forest
products. However, for the manager interested in improving the health of the
forest in order to help it better ward off insect attacks in the future, it is
important to understand some of the stand dynamics involved; such as the
relation between radial growth and stand stocking as affected by the
infestation.
Table 1. shows least-squares regression equations for 10-year mean plot
radial growth as a function of the natural log of the number of trees per
hectare hreater than 9 cm in diameter, by state of infestation (van Hees, in
press). Note that the values of the regression intercept decrease steadily
from potential infestation to past infestation. This pattern among the
estimated relations indicates that as the infestation progresses mean plot
radial growth decreases.
Table 1. Regressions of plot mean spruce radial growth on plot stocking
of all species > 9 cm dbh, Kenai Peninsula, Alaska, 1987.
Category Regression relation R-squared n
-------- ------------------- --------- ---
Potential y = 35.955 - 4.269 ln(tph9) 0.514 11
Ongoing y = 24.483 - 2.362 ln(tph9) 0.265 22
Past y = 14.323 - 0.922 ln(tph9) 0.112 25
Hard et al. y = 12.961 - 1.360 ln(tph9) 0.671 25
The possibility that stand density differences might account for radial
growth differences seen here was addressed by examining plot mean number of
live trees per hectare by stage of infestation. This was live-tree stocking
by all species, not just the white spruce.
T-tests indicated the differences between the means were not different from
zero at the 0.05 level of significance (van Hees, in press). Also, except for
one plot in the ongoing infestation group, all plots used for the three stages
of infestation were on sites capable of producing between 20 and 50 cubic feet
per acre per year. So, the plot groupings used for each of the three
infestation stages are similar in that plot mean stand density levels are
likely the same and the site qualities are essentially the same.
Stand stocking level differences then, are likely not the only important
explanatory variable for changes in the estimated regression relations.
Effects of the spruce beetle infestation are also consistent with patterns
observed in the estimated regression relations.
For verification, the regression relations developed here were compared
with an estimate made by Hard, who conducted small-scale studies of the same
infestation (Hard et al., 1983). Hard examined the relation between mean
cumulative 5-year radial growth of spruce on each plot and live-tree stocking
in an ongoing infestation on the Peninsula. Hard's result, in magnitude, is
nearly one-half that found for the ongoing infestation category in my study.
This is because Hard's estimate of radial growth was for a 5-year period as
opposed to the 10-year period used in this study.
Although the R-square of the estimated regression relation in Hard's study
is higher, the similarity of estimated regression coefficients indicates the
inventory data may provide managers with estimated relations that would not be
wildly divergent from true relations. So, the inventory data verify what is
intuitively reasonable. As stocking and stress from attack go up, radial
growth goes down. But now the manager would have a numeric model to work
with.
SUMMARY
What can a resource manager do with this kind of information? Some
questions that can be addressed using point-in-time forest inventory data
include: "Do we need to be concerned that the infestation needs attention?
How much area are we going to be dealing with? What are the product sizes and
quantities that could be available?"
At this point, the purpose of this paper has been demonstrated. One can
conduct an extensive forest inventory and from it, garner information that
will provide a manager with material regarding the current state and the
likely near-term future state of an infestation, some idea of expected forest
products flow, and an ability to model various stand dynamics in the face of
an infestation.
METRIC EQUIVALENTS
1 inch = 2.54 centimeters
1 acre = 0.4047 hectare
1 cubic foot per acre = 0.07 cubic meters per hectare
LITERATURE CITED
Hard, J. S., R. A. Werner and E. H. Holsten. 1983. Susceptibility of white
spruce to attack by spruce beetles during the early years of an outbreak
in Alaska. Can. J. For. Res. 13:678-684.
van Hees, Willem W. S. 1992. An anlytical method to assess spruce beetle
impacts on white spruce resources, Kenai Peninsula, Alaska. USDA For.
Serv. Res. Paper PNW-RP-446. 15 p.
van Hees, Willem W. S. and Edward H. Holsten. [In press] An evaluation of
selected spruce bark beetle infestation dynamics using point-in-time
extensive forest inventory data, Kenai Peninsula, Alaska. Can. J. For.
Res.
van Hees, Willem W. S. and Frederick R. Larson. 1991. Timberland resources
of the Kenai Peninsula, Alaska, 1987. USDA For. Serv. Resource Bull.
PNW-RB-180. 56 p.