#  **FINAL STUDY PLAN** -- 20 August 2000

**PROJECT TITLE:** Assessing wildland fire impacts on the winter habitat
use and distribution of caribou within Alaska's boreal forest ecosystem

**INVESTIGATORS: LAYNE ADAMS**, Wildlife Research Biologist,
USGS-Biological Resources Division, Alaska Science Center -- Biological
Science Office, Anchorage, Alaska

> **WILLIAM COLLINS**, Wildlife Research Biologist, Alaska Department of
> Fish and Game, Anchorage, Alaska
>
> **BRUCE DALE**, Wildlife Research Biologist, Alaska Department of Fish
> and Game, Anchorage, Alaska

## KYLE JOLY, Wildlife Research Biologist, USGS-Biological Resources Division, Alaska Science Center -- Biological Science Office, Anchorage, Alaska

##### 

##### SUMMARY

The US Geological Survey-Alaska Science Center -- Biological Science
Office (ASC-BSO) and the Alaska Department of Fish and Game-Division of
Wildlife Conservation (ADF&G) will conduct a 5-year investigation of the
impact of boreal forest fires on the Nelchina Caribou Herd (NCH) in
south-central and interior Alaska. This collaborative effort will
evaluate relationships between fire history and lichen abundance;
caribou habitat selection relative to lichen abundance; and caribou
nutritional performance relative to habitat selection, lichen abundance,
and spatial distribution. Results of this study will provide information
directly applicable to caribou and fire management in Alaska.

##### Problem Statement

# 

# Caribou wintering in boreal forest ecosystems of Alaska forage primarily on "climax" stage fruticose lichens. Wildland fires, however, chronically burn boreal forests, reducing the availability of forage lichens for decades. In addition, prescribed fires have been implemented to reduce fire hazards, restore biodiversity or enhance moose habitat. 

Since the early 1900s, wildland fire has been implicated in caribou
population declines. Numerous studies reveal reduced lichen
availability, long lichen recovery periods, and caribou avoidance of
recently burned winter ranges. However, direct evidence for fire induced
population declines is notably lacking. Moreover, researchers have long
debated the importance of lichens to caribou. Although lichens dominate
winter diets when available, examples of robust caribou populations
utilizing lichen-poor ranges suggest that high-quality summer range or
alternate winter forages can supplant lichen-rich winter range.

In addition, numerous investigators suggest that fire may rejuvenate
older lichen ranges by favorably altering moss-lichen relationships,
reducing overstory, or removing decadent lichens. Fire may also alter
caribou movement patterns, thereby allowing recovery of over-grazed
lichen ranges. Lastly, fire likely enhances summer range through
nutrient turnover and increased quality and abundance of vascular
forages. Contrary to assertions that fire is detrimental to caribou,
these mechanisms suggest that wildland fire may play a role in
maintaining caribou winter range or enhancing nutritional status.

The NCH provides ideal opportunities to investigate many aspects of the
influences of boreal forest fires on caribou. The NCH ranges over a
large portion of south-central Alaska that is influenced by a variety of
prescribed and wildland fire management regimes. During the last 2
decades, the herd shifted its primary winter range from an area of very
low fire frequency to a highly fire-influenced system north of the
Alaska Range. This current winter range has limited vegetation diversity
other than that generated by wildland fire occurrence. In addition, 2
prescribed fires to enhance moose habitat have recently been conducted
on this winter range and another is scheduled for a portion of the
summer range. These characteristics, along with a substantial history of
research and relatively good accessibility, make the NCH a logical
choice for investigating the relationships between fire and caribou.

Management of the NCH generates substantial public interest because of
its accessibility to a large portion of the Alaskan public for both
subsistence and general uses. Understanding caribou/fire relationships
on ranges of the NCH will provide information for herd management
locally, and fire management throughout the boreal forest ecosystem of
Alaska and Yukon.

OBJECTIVES

The overall goal of this research is to improve our understanding of the
effects of fire on caribou populations utilizing boreal forest winter
ranges. To reach that goal we provide the following specific objectives
and hypotheses:

1\. Determine the relationships between fire history and forage lichen
abundance and condition.

> H1. Forage lichen abundance and productivity increases with stand age
> to roughly 50-100 years, then declines.
>
> H2. Fire promotes lichen growth by reducing overstory or moss and
> increasing soil temperatures in declining and grazing-depleted lichen
> stands.

2\. Investigate the importance of lichen, and therefore fire history, in
determining caribou habitat selection.

> H3. Historic winter ranges in the Nelchina Basin have lower lichen
> abundance than currently used winter ranges north of the Alaska Range.
>
> H4. Caribou avoid burned areas for decades post-fire and select for
> older stands with abundant forage lichens.
>
> H5. Winter movement patterns are influenced by lichen abundance.

3\. Evaluate relationships between winter habitat selection of caribou
and their nutritional performance.

> H6. Caribou utilizing lichen-rich winter ranges gain more or lose less
> weight than caribou utilizing lichen-poor ranges.
>
> H7. Caribou weight gain on preferred winter ranges is independent of
> caribou density on those ranges.
>
> H8. Weight change is related to the proportion of lichen in the winter
> diet of caribou calves.
>
> H9. Winter weight change is independent of summer weight gain.

4\. Evaluate the influences of various fire management regimes on
caribou.

> H10. Altering the frequency of fire recurrence (return interval) on
> winter range influences caribou through changes in availability of
> forage lichens.

##### Background

# **[Summary of caribou/fire/lichen research]{.smallcaps}**

Fires are normal, recurring events in boreal forest ecosystems and are
instrumental in maintaining productivity and biodiversity. The
importance of fires in shaping the vegetative landscape of interior
Alaska cannot be overestimated. Fire recurrence relates to age of
vegetation, physiography and local environmental conditions (Rowe *et
al.* 1974). Patterns of post-fire succession (Van Cleve and Viereck 1981
and 1983) and fire cycle effects on the forest mosaic are well described
for boreal forests (Bergeron and Dansereau 1993). The effects of these
processes on caribou populations, however, are poorly understood.

Lichens, particularly *Cladina* spp. ("caribou lichens"), are a major
source of winter food for most continental populations (Kelsall 1960,
Miller 1976, Klein 1980, Russell and Martell 1984, Thomas and Barry
1991, Thomas et al. 1998b). On Alaskan winter ranges, lichens comprise
60-80% of the winter diet (Adams and Connery 1983, Boertje 1984, Boertje
and Gardner 1998). Wildland fire reduces abundance or kills forage
lichens and lichen woodlands are susceptible to frequent burning
(Auclair 1983). Moreover, caribou forage lichens are associated with
late stages of boreal forest succession (Klein 1982) and require long
periods of recovery following fire or other disturbance (Viereck and
Schandelmeier 1980, Thomas and Kiliaan 1998).

Research in the 1960s in northern Canada suggested that fires had a
detrimental effect on caribou populations due to loss of mature forest
stands with substantial standing crops of lichens (Kelsall 1960, Thomas
1969, Scotter 1965, 1970). Conversely, studies reported by Skoog (1968)
and Bergerud (1972) for Alaska and Newfoundland, respectively, indicated
little or no impact of fire on population dynamics of caribou. These
divergent findings precipitated evaluations of the influence of
fire-depletion of lichen ranges on winter habitat use (Miller 1976,
1980, Thomas 1991, Thomas et al*.* 1998a).

Several researchers suggested that caribou shift their distribution in
response to burn patterns. Thomas *et al.* (1998a) found that caribou of
the migratory Beverly Herd in the Northwest Territories readily traveled
through most burns, but spent little time in them and avoided very large
burns (\>50,000 ha) on the periphery of the winter range. The authors
suggested that the herd expanded its range to replace burned portions of
the traditional range. Schaefer and Pruitt (1991) studied effects of
fire on resident woodland caribou in Manitoba and concluded that quality
and accessibility of winter forages in burned upland habitats declined.
They attributed this decline to loss of lichens (*Cladina* spp.),
increased thickness and hardness of snow, and accumulation of deadfalls.
By five years post-fire, caribou had abandoned burned areas completely
and remnant lichen populations were exhausted. During this post-fire
period, they observed a three-fold increase in area used during winter
and a decline in caribou population size. They concluded that areas
burned within 50 years were not suitable winter habitat. These results
suggest substantial negative impacts of fire on caribou through
increased migratory costs and reduced foraging efficiency.

Other researchers argue that positive impacts of fire outweigh the loss
of lichen stands. Because extremely old stands have reduced lichen
productivity, Klein (1982) asserted that fire is essential in the
recycling of nutrients and maintaining vegetative productivity and
ecological diversity. In addition, because caribou may avoid recent
burns, fire may maintain the productivity or allow recovery of winter
ranges (Lutz 1956, Ahti and Hepburn 1967, Rowe et al. 1975, Kershaw
1977). Fire also can enhance the quality and abundance of summer forages
(Schaefer and Pruitt 1991). Thus, it seems that the availability of
various successional stages throughout caribou range, such as may occur
under natural disturbance regimes, assures long-term productivity of the
ecosystem for caribou.

The existing body of research clearly indicates that wildland fire has
enduring influences on lichen abundance in boreal forests. Following
decades of extensive fire suppression efforts, there can be little doubt
that past fire management policies influenced the present winter
habitats of many Alaskan caribou herds. Although suppression policies
adopted in the mid 1980s promise a return to natural fire regimes for
remote areas, suppression and management efforts inevitably increase
with human settlement and development. Consequently, fire management
policies will, at a minimum, continue to be relevant to caribou
management. Yet despite these considerations, the net impacts of fire on
forage lichen abundance remain debatable. The effects of lichen
abundance on caribou distribution are, likewise, poorly understood.
Moreover, no attempt has been made to link fire-influenced variation in
lichen abundance or caribou distribution to the fitness of individual
caribou or the dynamics of caribou populations.

# **[Fire and abundance of forage lichens]{.smallcaps}**

Abundance of forage lichens is closely related to post-fire stand-age,
however, regional colonization and growth rates vary substantially
(Thomas and Kiliaan 1998). Lichens exhibit distinct periods of growth
and lichens in the oldest growth stage die at the base at rates equal to
or faster than the accumulation of new growth at the tips (Andreev
1954). Thus, lichen abundance increases with stand-age for several
decades after colonization. Numerous investigators suggest that forage
lichen abundance eventually declines in very old black spruce stands due
to increased moisture and muskeg formation or shading from closure of
the spruce/shrub canopy (Andreev 1954, Kershaw and Rouse 1976, Maikawa
and Kershaw 1976, Miller 1976b). Depending on fire frequency, declining
lichen abundance in very old stands could tremendously influence winter
forage availability. Empirical data addressing these relationships are
lacking for Alaskan spruce forests and time-scales vary widely among
locales (Andreev 1954, Tengwall 1928 cited in Hale 1973).

Specific data regarding colonization, growth characteristics, and
abundance of lichen following fire are fundamental to understanding
fire-influenced changes in winter forage availability for caribou.
Gathering this data in a reasonable time frame entails comparing lichen
characteristics in stands that vary in age post-fire, but are otherwise
very similar. The current winter range of the NCH is particularly suited
to that task. It consists of a mosaic of black spruce stands across a
large area of gentle topographic relief. Age of a stand post-fire is
likely the dominant landscape characteristic influencing lichen
abundance.

Caribou grazing also influences lichen abundance (Miller 1976a, Ouellet
et al. 1993). Similar to fire-depleted stands, intense grazing and
trampling can reduce live lichen biomass for decades. However, the
persistence of lichen fragments and intact plants in grazing-depleted
stands may short-cut the early succession and colonization stages
characteristic of fire-depleted stands. Therefore lichen biomass on
grazing-depleted stands may recover more quickly than on fire-depleted
stands.

Conversely, Viereck and Schandelmeier (1980) suggested that fire
increases lichen abundance in old stands where moss layers or shrub and
spruce overstory competitively inhibit lichen growth. Under this
scenario, competition is expected to slow or stop the recovery of
grazing-depleted stands. Because fire would mitigate the competitive
effect, fire might actually expedite, or even be requisite, in the
recovery of some grazing-depleted lichen ranges. While existing data
allows only rudimentary speculation, it is apparent that grazing and the
dynamics of associated vegetation may fundamentally influence the
consequences of fire on forage lichen availability. Investigating these
processes can be accomplished through *in situ* experiments that
evaluate lichen growth rates for various fire and grazing treatments.

# **[Lichen abundance and caribou habitat selection]{.smallcaps}** 

Pruitt (1959) and Bergerud (1974) asserted that habitat selection should
be based primarily on forage abundance and secondarily on environmental
conditions. Thomas and Kiliaan (1998) described a clear peak and decline
in forage lichen biomass as post-fire age of jackpine stands increased
in Canada. Given these relationships, caribou should select for stands
in lichen-rich age ranges and avoid younger or older stands. Strong
selection for stands of certain ages implies that various fire
management regimes can greatly influence the accessibility of caribou to
humans, if not the dynamics of caribou populations. Conversely, lack of
selectivity for lichen abundance would suggest minimal impact of fire
management decisions on caribou.

To date, studies of barren-ground caribou distribution indicate mere
avoidance of recent burns rather than strong selection for lichen
abundance. Ouellet et al. (1993), Saperstein (1993), and Thomas et al.
(1996) explained that caribou foraging dynamics and environmental
variation substantially influenced habitat suitability. For example,
heterogeneity in lichen distribution likely affects foraging efficiency
and foraging risk, which in turn influence habitat selection. Such
factors may indeed influence habitat selection more than lichen
abundance.

Other environmental variation may obscure selectivity for lichen
abundance. Avoidance of recent burns by woodland caribou was partially
attributed to increased foraging and movement costs due to increased
deadfall trees and snow hardness (Schaefer and Pruitt 1991). The winter
range currently used by the NCH consists of low hills and lowlands
dominated by open canopy black spruce. For this winter range, deadfalls
may not affect habitat selection because of the small size and low
densities of trees and the tendency for burned trees to remain standing.
Likewise, snow characteristics may not vary dramatically with stand-age
(Schaefer 1996). Reduced variation in these factors should make
selectivity for lichen abundance more detectable. Lack of selectivity
for lichen abundance on this range would provide strong testimony for
the importance of factors other than lichen abundance in determining
caribou habitat selection and distribution.

Detecting selection for lichen abundance becomes difficult if additional
factors influence habitat selection, but on different spatial scales. No
single spatial scale is likely appropriate for all of the relevant
ecological processes (Bowers 1997). Moreover, strong interaction among
two co-factors may be expressed on a third spatial scale. For example,
if wolf predation risk and post-fire stand-age equally influence habitat
selection, the appropriate scale becomes the intersection of the pack
territory and burn mosaics. Because the resulting scale is finer,
evaluating habitat selection at the scale of either factor might be
inconclusive, even though each strongly influences habitat selection.
For evaluating the influence of lichen abundance on habitat selection,
this problem can be dealt with by exploring selectivity on a variety of
spatial scales. The likelihood of Type II error from scalar effects of
co-factors is reduced by bracketing the spatial scales at which
selectivity for the main effect is evaluated.

The size and variability of annual ranges and the mobile nature of large
barren-ground caribou herds also complicate analyses of caribou habitat
selection. For example, the NCH ranges over 68,000 km^2^ consisting of
diverse habitats ranging from forested lowlands to the alpine tundra of
3 mountain ranges. Large portions of this range are greater than 50 km
from the few year-around roads and trails. Caribou distribution across
this area can vary dramatically within and among years.

These conditions complicate the estimation of habitat use required for
robust analyses of habitat selection (Thomas and Taylor, 1990). Aerial
surveys (Jakimchuk and McCourt 1975, Stardom 1975), conventional
radio-telemetry (Miller *et al.* 1975), and satellite telemetry (Pank
*et al.* 1985, Harris *et al.* 1990) have all been used, however, all
have analytical or logistical shortcomings. Conventional aerial
telemetry and aerial surveys provide data for only a very short time
period or time of day, when habitat use over a longer period, such as a
season or year, is usually desired. Satellite telemetry provides
frequent locations, but accuracy is poor, information on activity and
group size cannot be obtained, and the number of animals in the sample
is usually low. GPS telemetry has many of the same limitations as
satellite telemetry, but can be very accurate. An approach employing a
combination of conventional and GPS telemetry can mitigate many of these
shortcomings and provide additional flexibility in assessing the effects
of spatial scale.

Assessing lichen availability for caribou is similarly challenging. Data
at appropriate spatial scales are required for very large areas (Adams
and Connery 1983). Ground-based estimations of availability are time
intensive and thereby limited to small areas and fine spatial scales.
Satellite-based remote sensing data is often implemented to mitigate
those limitations (Russell et al. 1983, Adams and Connery 1983), however
it is unclear whether that technology can adequately sense low biomass
groundcover such as forage lichens. Digital applications of fine-scale
aerial imagery have recently been implemented as efficient and flexible
methods for assessing vegetation coverage over large areas. Forage
lichens are highly visible from small aircraft and lichen biomass
strongly correlates with coverage (Thomas and Kiliaan 1998). Developing
aerial videography and photography techniques appropriate for forage
lichens should provide useful methods for estimating lichen availability
for caribou. In addition, the application of those methods would provide
a data set suitable for the evaluation of satellite-based techniques.

**[Wildland fire and caribou nutritional status]{.smallcaps}**

Evidence that fire influences barren-ground caribou population dynamics
is circumstantial and no direct mechanisms for population change have
been demonstrated. Poor nutritional condition resulting from reduced
lichen availability is the most obvious potential consequence of fire on
winter range, but selection for particular habitats or forages does not
necessarily indicate direct nutritional consequences. If alternate food
resources provide sufficient nutrition, fires on winter range may affect
distribution without negatively impacting nutritional status.

Determining whether lichen availability influences winter nutritional
status is problematic. First, the scale at which availability might
influence nutrition is unknown. Where should lichen abundance be
measured: the entire caribou range or within late-winter feeding
craters? Furthermore, local snow conditions and numerous other spatially
explicit factors also affect nutritional status. Identification of an
appropriate spatial framework is simply impossible. A hierarchical
approach that first identifies important spatial scales is warranted
(Coulson et al. 1997). Essentially, it is first determined if there are
spatial patterns in nutritional performance at *any* spatial scale. Once
identified, spatial trends in causal factors, such as lichen abundance,
are evaluated relative to the observed patterns in nutritional
performance.

One such spatial scale can be identified *a priori*. During the last
several years, roughly 80% of the NCH has wintered on relatively
lichen-rich ranges north of the Alaska Range. The remainder of the herd
continues to use historically heavily grazed and currently lichen-poor
winter ranges in the Nelchina Basin (Lieb et al. 1984). Comparisons of
the nutritional performance of caribou and lichen abundance on these two
ranges will provide useful insight to the importance of lichen forages
on a very broad scale. Identification of other spatial trends in
nutritional performance will invite appropriate evaluations of lichen
availability, as well as other factors, across the observed trends.

With the application of stable isotope analyses of blood samples
collected from individual caribou, it may be possible to categorize
winter lichen consumption and relate that to nutritional performance of
individual caribou. Stable isotope analysis, a rapidly emerging
technology, is based on the differential use of naturally-occurring
elemental isotopes in biological processes (Tieszen 1991). Carbon
cycling in lichens differs from other caribou forages that employ the
C~3~ photosynthetic pathway. Similar to plants using the C~4~ pathway,
reduced depletion of ^13^C in lichens should result in a unique isotopic
signature (Tiezen et al. 1998). Therefore, variation in the ratios of
carbon isotopes from caribou blood samples (L. Adams, unpubl. data)
should reflect the proportion of lichen in their winter diets. Although
these underlying relationships need to be validated, this technique
shows promise and would allow for direct comparisons of winter lichen
consumption with weight change among individual caribou.

Growth of young females (5-17 months-of-age) is a sensitive and
therefore desirable indicator of nutritional performance because their
body weight changes rapidly through a single season and their nutrition
is largely free of maternal influence. Further, age of sexual maturity
is strongly influenced by body weight at 17 months-of-age (Adams and
Dale 1998). For caribou, substantial variation in weight gain among
individuals and among cohorts has been observed in this sex-age class.
Individual variation in seasonal growth rates within cohorts should
reflect quality and availability of food experienced by each individual,
while variation among cohorts should reflect population-wide trends of
or influences of stochastic environmental variation on nutrition during
a particular season.

Determining winter weight dynamics of female calves will require the
capture and handling of individuals prior to the onset of winter (at 5
months of age, early October) and again at the end of winter (at 11
months of age, mid April). The initial weight at 5 months of age will
also allow for comparing the interactions of nutritional performance of
calves during their first summer with that over the winter. Summer
nutritional performance may carry through and have greater influence on
late winter condition than winter foraging conditions and may influence
winter habitat selection. Summer nutritional performance alone has been
linked to substantial population declines (Couturier et al. 1988).

Winter nutritional performance may also influence subsequent summer
weight gains and ultimately reproductive performance of individuals
(Adams and Dale 1998). By weighing individuals again at 17 months (early
October), we will have the full suite of information necessary to
evaluate the importance of winter nutritional performance and habitat
quality relative to summer nutrition.

Impacts of fire on caribou may not be limited to nutritional
consequences. Caribou employ specific spatial strategies to reduce risk
of predation and changes in caribou distribution may influence survival
rates (Bergerud 1980, Dale et al. 1994). Spatial strategies such as
predator "swamping" or "spacing away" imply strong trade-offs between
predation avoidance and nutritional performance. However, virtually
nothing is known about these relationships. Fire clearly influences
spatial distribution on winter range as evidenced by avoidance of recent
burns and expanded winter use areas. Selection for particular age stands
would indicate even greater influence of fire on spatial distribution,
especially during late winter when caribou vulnerability is high.
Identifying spatial patterns in nutritional performance and survival of
caribou, and linking those patterns to habitat characteristics, provides
the ecological framework necessary to evaluate influences of fire, or
other environmental disturbance, on caribou populations.

### Evaluation of fire management regimes on caribou populations

The data collected in this study will provide information for modeling
various caribou/fire/lichen relationships. Spatially explicit models of
influences of fire management regimes on lichen abundance will be
particularly useful for managers. In addition, this study will provide
data for modeling a variety of processes, from deterministic models of
lichen abundance and caribou weight gain, to stochastic time-series
models of interactions among fire, weather and caribou dynamics. Our
research approach is especially conducive to these types of evaluations
because, fire, lichen abundance, habitat selection, nutritional
performance, and survival are simultaneously evaluated within a very
flexible spatial framework.

# **Study Area**

The NCH numbered more than 70,000 in the early 1960's (Siniff and Skoog
1964), but declined through the early 1970's, reaching a low of
approximately 7,000 individuals in 1972 (Bos 1975, Lieb et al. 1984).
The herd again rebounded to 45,000 by 1992 (McCarthy 1992). The herd is
currently estimated at about 31,000 caribou (R. Tobey, unpubl. data.).
The mechanisms behind these population fluctuations have been topics of
vigorous debate (Van Ballenberghe 1985, Bergerud and Ballard 1988).
Factors that have been suggested are range degradation, severe weather,
predation, and hunting (Bergerud 1978, Doerr 1979, Van Ballenberghe
1985, Bergerud and Ballard 1988).

The range of the Nelchina herd is generally bounded by the Talkeetna
Mountains to the west, the Alaska Range to the north, the Wrangell
Mountains to the east and the Chugach Mountains to the south. This area
is commonly known either as the Copper River or Nelchina Basin
(elevation 500 -- 600m). Two major river systems, the Copper and
Susitna, drain the basin. Caribou are commonly found at elevations up to
1,500m (Van Ballenberghe 1985). The current range of the NCH encompasses
approximately 68,000 km^2^. For the last several years, the majority of
the herd has wintered northeast of the Alaska Range. These wintering
grounds were not used historically by the NCH (Lieb et al. 1984). In
early spring, the herd leaves the wintering grounds and heads west to
the calving grounds. Calving typically takes place in late May in the
Oshetna and Black River drainages on the east side of the Talkeetna
Mountains. After the post-calving aggregation, the herd has a tendency
to spread out in late summer and fall, and can be found almost anywhere
in the Nelchina Basin. Grouping begins again in late September when the
herd starts migrating for the winter range.

The summer range is primarily above timberline. Shrub and alpine
communities dominate this section of the range. Low topographic
variation, relatively low elevation, numerous lakes, ponds, and streams,
and extensive black spruce stands characterize the historic winter range
in and around the Lake Louise flats. Lichen, once abundant in the
region, is now considered sparse (Lieb et al. 1984). Wildland fires in
the Nelchina Basin are infrequent due to a cooler and wetter climate
that results in fewer lightning-caused ignitions and poor burning
conditions. Most of the herd currently winters northeast of the Alaska
Range in the Ladue and Dennison River drainages, as well as the Northway
flats. The Ladue and Dennison drainages can be characterized as rolling
hills with typical boreal forest vegetation. Alpine communities are
present, but they are not common. The Northway flats also contain boreal
forest, but muskegs are more common due to elevation and topography.
Large fires occur frequently in these wintering grounds, as they do
throughout boreal forests of interior Alaska and Yukon.

The continental climate of the Nelchina Basin provides between 59 and
116 frost-free days, with snowfall accumulation from 84 to 417 cm
(Bergerud and Ballard 1988). Snow depths vary greatly from year to year
and place to place depending altitude, aspect, and other topographic
characteristics, but generally reach depths of 25 cm by mid-November, 50
cm by December, and 75 cm irregularly (Skoog 1968). Temperatures can
vary throughout the year from --50 to 32^O^ Celsius.

### 

### Methods

# 

# **[Fire and abundance of forage lichens]{.smallcaps}**

Lichen abundance -- stand age data will be collected at 120 caribou
locations and 120 random locations throughout the winter range. Forage
lichen biomass and productivity will then be modeled as functions of
coverage, depth and age of stand.

Age of each stand will be determined from sections of spruce trees
(Madany et al. 1982, McBride 1983, DeVolder 1999) and Alaska Fire
Service (AFS) maps and databases. Lichen cover will be estimated at 2m
line intercepts along 60m transects, with coverage being classified as
either open to the sky or covered by taller vegetation.

# Depth of forage lichen mats will be determined in all ground-truthed sites by collecting and later measuring 5cm diameter plugs of forage lichens taken closest to the midpoint of each line intercept. Length and age of living and dead thalli from the *Cladina* spp*.* lichens will be measured and related to the ages of stands as determined from BLM fire histories or from ages of spruce within the stands.

**Development of digital imagery techniques**

We will evaluate aerial imagery to determine which image format
(photography or videography), image size (altitude), spectral ranges and
airspeeds best allow estimation of forage lichen coverage. This work
will be done in conjunction with determining lichen abundance -- stand
age relationships in road accessible stands. To evaluate the technique,
we will compare estimates of lichen cover from aerial images to lichen
cover estimates obtained from ground sampling. A square block of 9
adjacent 10x10m quadrats will be established in each of 5-7 stands
representing a broad range of lichen abundance. The nine adjacent 10x10m
quadrats will provide a nested series of 10x10, 20x20, and 30x30m plots
for efficient evaluation of aerial images at different scales.

The corners of the center quadrat of each cluster will be marked with
surveyor's section flagging so as to be distinguished from the air.
Lichen cover and biomass will be estimated as described above. The
center 10x10m quadrat, 20x20m and 30x30m nested clusters will then be
recorded by videography from a helicopter hovering at altitudes from 30
-- 200 feet. The nested clusters will also be photographed with a 9x9mm
high-speed camera and high-speed color film, as well as videography,
from 500, 1200, and 2000 feet from a fixed-wing aircraft. Altitudes
above ground level will be determined by radar altimeter. Images will be
geo-referenced by ADF&G software (Photoman) or GPS and video clock
times. Film negatives will be scanned at 1000dpi and saved to CD ROM.
Lichen coverage will then be determined by conventional photo
interpretation or by digital classification.

Total coverage and variability of estimates from imagery will be
compared with those of ground samples to determine the highest altitude
and means by which a minimum coverage of about 10-15% can be detected.
Once the aerial imagery technique is selected, it will be ground-truthed
across the study area. Aerial images will be taken near each of the 120
random and caribou location ground transects (see above). Estimates of
lichen cover from imagery will then be compared to cover and biomass
estimates collected during ground sampling. If necessary, other
variables, such as shrub or tree cover or topography, may be used to
improve lichen estimates from aerial imagery via appropriate regression
techniques.

**Factors influencing lichen recovery and regeneration***.*

Possible effects of organic substrates and nature of disturbance on
early lichen recovery will be examined experimentally by "seeding"
lichen fragments into burned and unburned sphagnum moss; burned and
unburned duff or mor; and spruce-shaded and unshaded duff or mor. All
plots will be selected for uniformity in vegetation type,
microtopography, and exposure. Burned plots will be prepared under
uniform conditions and exposure to a weed burner. A portion of unburned
plots will be cleared of existing lichens to make room for placement of
fragments. Existing mats of *Cladina* spp. will be left undisturbed in a
portion of each plot to provide a reference to growth rates of
established lichen. Two types of fragments of *C. rangiferina*, and *C.
arbuscula/mitis* will be prepared by cutting 1cm lengths of
photosynthetically active tips and 1cm lengths of 5- to 10-year-old
thallus. Fragments will then be spaced at 5 cm intervals in 1 m lines
and protected by cages constructed of concrete reinforcement mesh.
Fragments will be measured with calipers about the end of June annually.
Vegetation cover in the protected plots and in adjacent unprotected,
paired plots will be determined from digital imagery or by point grid
sampling when lichens are measured for growth*[.]{.smallcaps}*

### GIS development

Numerous GIS themes will be developed and/or implemented in this
research project. The GIS platform is especially appropriate for
evaluating habitat characteristics across the large and diverse areas
used by Nelchina caribou.

*Fire history mapping*: Substantial fire history information is already
available from AFS as digital map products and databases. We will fine
tune the existing GIS coverage by searching AFS files for additional
fires and adding them to digital coverages. An effort to map all fires
greater than 4.05 km^2^ (1000 acres) will be made. However, information
about numerous fires has been lost over the years and fires under 4.05
km^2^ (1,000 acres) in size were often not recorded. Almost no
information is available for fires dating before 1950. Potential for
cooperative agreements with other agencies, such as NASA (Kasischke et
al. 1993, Kasischke and French 1995), to develop fire history maps using
remote sensing techniques are being pursued.

*Landcover databases*: Numerous existing databases developed by USGS,
FWS, NPS, and NASA cover portions of the NCH range. Vegetation maps
based on AVHRR (1 km resolution), Landsat TM (30m resolution), and/or
Landsat MSS (50m resolution) data may be used to analyze habitat use in
relation to availability.

*Topographic databases*: Twelve USGS 1 degree Digital Elevation Models
(DEMs) will be projected into a seamless, 30m grid using cubic
convolution sampling in Arc/Info. This DEM will then be used to derive
other products, such as a slope and aspect grids and a shaded relief
using both Arc/Info and ArcView.

*Lichen distribution*: The possibility of developing a model to map
lichen distribution across the range of the NCH will be investigated.
This model would likely incorporate most, if not all, of the
aforementioned GIS coverages. Vegetative analyses conducted during this
project will provide information about the importance of fire history,
habitat type, elevation, slope, and aspect to lichen abundance. The
categories (i.e., a particular stand age) of each of the individual
coverages would be ranked in terms of lichen abundance. The ranked
coverages would then be overlaid to develop a separate coverage modeling
where high lichen abundance is predicted.

### Lichen abundance and caribou habitat selection

We plan to utilize GPS telemetry and conventional telemetry to gather
information on seasonal distribution and movements of caribou. That
data, in concert with GIS coverages described above and additional
sampling of forage lichen abundance utilizing fine-scale aerial imagery,
will allow us to evaluate habitat selection by caribou on a variety of
spatial scales.

# **Caribou capture, handling and telemetry** 

*Capture and Handling*. Caribou will be captured via standard helicopter
chemical immobilization techniques (Adams et al. 1995, 1997). Caribou
will be weighed via electronic load cell and skeletal measurements will
be taken. Rump fat depths of adult females will be determined by
ultrasonography (Stephenson et al. 1998) to assess nutritional
condition. Blood samples will be taken for disease screening, analysis
of stable isotopes (Tiezen 1991) and pregnancy determination. Each
individual will be fitted with a conventional or GPS radiocollar.

*GPS telemetry*. GPS telemetry will be employed for estimating habitat
selection at temporal scales that are not possible with conventional
telemetry and evaluating the influences of relocation frequency on our
estimates of caribou distribution and habitat selection. Beginning in
October 1999, we will maintain a sample of 20-25 female caribou fitted
with GPS radiocollars that also have conventional mortality-sensing
telemetry beacons. Onboard GPS receivers will collect locations every 7
hours and store those locations to memory included in the collar. In
April and October, GPS-instrumented caribou will be recaptured to
download stored location data and to replace batteries*.*

*Conventional telemetry.* In addition to the GPS instrumented
individuals, we will maintain a sample of about 100 caribou with
conventional radiocollars. Therefore, our sample of caribou instrumented
for conventional telemetry should equal about 120 animals in each winter
of the study. In addition, approximately 80 instrumented caribou from
the Mentasta herd, which shares winter range with the Nelchina herd,
will be used to augment sample sizes where necessary. All instrumented
caribou will be located at least monthly by aerial radiotelemetry.
Observers will record location, as determined by aircraft-born GPS
units, and group size for all observations of instrumented caribou.
Mortalities will be noted during radiotracking flights and mortalities
will be investigated via helicopter as soon as practical.

**Habitat selection**

# Selectivity will be evaluated on three scales. The broadest scale will be range-wide and will address the role of lichen abundance in the shift to the current winter range from winter ranges used in the early 1980s in the Nelchina Basin. The intermediate scale will address the influence of fire on winter range by evaluating selection for particular aged stands within the current winter range. The finest scale will examine how lichen abundance influences movement patterns of caribou within winter range. 

*Habitat Selection and lichen abundance on range-wide scale***.**
Habitat use will be determined by late winter (Feb -- Mar) conventional
telemetry locations collected during this study and from 1980-85
(Pitcher 1982). Availability of habitat types at a scale of 1 km^2^ will
be determined from the AVHRR vegetation classification map of Alaska.

Lichen abundance will be compared among the most preferred and most
utilized vegetation types for the 1980s data and the current study.
Lichen abundance will be estimated from aerial images or on-site
quadrats within late winter caribou concentration areas as defined by
the 50% probability contour of kernel home range estimates for each
sampling period. Arc-sine transformed estimates of lichen cover
estimates among current and early 1980s winter ranges will be evaluated
by ANOVA.

*Selectivity for post-fire age of stand*. Although a complete
understanding of fire history over the 68,000 km^2^ range of the NCH is
unattainable, we have substantial information on fire history,
particularly in the winter ranges currently being used north of the
Alaska Range. We believe the resulting GIS coverage of fire history,
although incomplete, will be adequate for evaluating selectivity
relative to stand-age.

Both GPS and conventional telemetry data, in separate analyses, will be
used to estimate use among known-age stands. To estimate the
availability of various post-fire age classes, we will delineate the
extent of winter range north of the Alaska Range as the area enclosed by
the 99% utilization distribution of all locations during November-March.
Selectivity relative to post-fire stand age will be estimated annually
and evaluated relative to factors that may induce variability among
years (e.g. snow conditions or other weather characteristics). If
appropriate, analyses will be pooled over all years. Selectivity
analyses will follow Manly et al. (1993). Lichen abundance-stand age
relationships will be estimated via aerial imagery to compare
selectivity related to stand age with patterns of lichen abundance.
Ultimately, a logistic regression model will be constructed to evaluate
influences of lichen abundance, stand age, land cover, landform, and
environmental parameters on selectivity.

*Movement patterns and lichen abundance*. To evaluate selection for
lichen abundance at a finer scale, we will explore relationships between
observed caribou movement patterns and lichen abundance. Preliminary
data from GPS collars deployed on caribou in the Fortymile Herd (K.
Joly, unpubl. data) indicate that winter caribou movements can be
categorized based on the direction and distances traveled between
sequential locations. Hourly relocations revealed directional travel
(migration-like movements that lasted several days to several weeks),
short-term localization (non-directional movements within a small area
lasting 1 day to 1 week), and long-term localization (non-directional
movement lasting a month or more). We hypothesize that lichen is more
abundant in areas where caribou localize.

Utilizing data from the movements of GPS-instrumented caribou during
winter, we will categorize locations by movement pattern as reflected in
relative densities of sequential point locations. Locations of
individual animals will be mapped and fixed-width kernel density
estimates will be calculated at each location (Silverman 1986). The
width of the smoothing factor will be adjusted so that a frequency plot
of the density estimates reveals about 3 modes which will describe the
mid points of the movement categories. If the frequency plots indicate a
lack of modality locations will be stratified into categories defined by
standard deviations from the mean density. Missing point locations will
be estimated by interpolation from the next successful attempt. Although
this will introduce error into the analysis, bias in habitat or
topographic related failure of GPS satellite acquisition would be
reduced.

Following categorization, we will randomly select 50 locations from each
movement category. Lichen abundance will be determined at each of these
locations via aerial imagery. We will use ANOVA to evaluate whether
arc-sine transformed lichen cover estimates vary among movement
categories.

In addition, we will use these data to test the hypothesis that lichen
cover at a caribou location influences the distance and change in
bearing traveled to the caribou's subsequent location. For this
analysis, we will employ multiple regression techniques thereby
facilitating the incorporation of other variables, such as temperature,
snow pack depth, and time-of-day, into the model. Indicator variables
will be transformed as appropriate for least-squares regression and
evaluated via stepwise selection (Neter et al. 1985). The response
variable will be a vector consisting of the distance traveled divided by
the relative change in heading (0-180 degrees) from the previous
movement.

### 

### Nutritional performance of caribou calves

As part of our conventional telemetry sample, we will annually capture
and radiocollar approximately 40 5-month-old female calves using
standard methods described above. These animals will be weighed,
measured, and blood-sampled. They will be recaptured in mid-April at
11-months-old and processed similarly. Weight change of these calves,
expressed as weight gain/loss or percent change over winter and arc-sine
transformed for parametric statistical analyses, will be the basis of
our evaluations of habitat effects on nutritional performance. Weight
change data will first be evaluated to detect variation among cohorts
(ANOVA) or relationships between fall body weight (i.e. summer weight
gain) and subsequent winter weight change (least-squares regression).

At the broadest scale, we will compare nutritional performance of
caribou calves that winter in the Nelchina Basin, on lichen-depleted
ranges, with those that winter north of the Alaska Range where forage
lichens are expected to be more abundant (ANOVA). At the next level,
caribou calves will be grouped based on their within-season spatial
distribution through hierarchical cluster analysis with average linking
(Coulson et al. 1997). Variation in weight change among clusters will be
evaluated by ANOVA with arc-sine transformation of the dependent
variable (Neter et al. 1985). In these analyses, scale can be adjusted
by varying the number of clusters. If regional variation in nutritional
performance is detected, then that variation will be evaluated relative
to lichen abundance based on models developed from satellite imagery
and/or fine-scale aerial imagery collected for selectivity analyses. At
the finest scale, we hope to be able to categorize the importance of
lichen in the winter diets of individual calves, based on stable isotope
analyses of blood samples, and compare that to their weight change.

# Regardless of scale, analyses to further explore variation in caribou nutritional performance will include other factors that may affect weight change. These variables include summer nutritional performance, relative caribou density, and environmental variables and will be incorporated into the cluster analysis through ANCOVA or evaluated separately through least-squares regression. Fall body weight will serve as a measure of nutritional performance during the preceding summer. An index of relative density will be developed for each individual by averaging the fixed kernel density estimates of each caribou among all monthly winter locations. Environmental variables will be derived from weather data collected by NOAA and FAA weather stations in Tok, Northway, Paxson, Glennallen, Eureka, Palmer, Talkeetna, and Cantwell and remote fire weather stations throughout the area.

# **[Evaluation of fire management regimes on caribou populations]{.smallcaps}**

Appropriate models will be developed to evaluate the influence of fire
management regimes on lichen abundance, suitability of boreal forest as
caribou winter range, caribou movements and distribution, nutrition and
survival trade-offs, seasonal range trade-offs, and caribou population
dynamics. These models will incorporate data from this and other
research where appropriate. The specific types of models constructed
will necessarily reflect the nature and significance of relationships
identified in this research effort (Starfield and Bleloch 1991).

A spatially explicit time-series model of the influence of various fire
management regimes on lichen abundance will be particularly useful for
managers. Fire recurrence will be varied over seasonal ranges of the NCH
to simulate the effects of various fire suppression regimes. Fires will
vary stochastically in size and shape within the parameters of previous
fires within that locale thereby creating a complete mosaic of stand-age
polygons. Lichen abundance within stands will reflect the age of the
stand and the area of boreal forest within the stand as determined from
AVHRR or other vegetation classification maps. Lichen removal from
grazing will be simulated using rates estimated by Arseneault (et al.
1997) adjusted for herd size and distributed among stands following
stand-age selection models developed from this study. Range-wide
abundance of forage lichens in boreal forests within the range of the
herd will be calculated for varying fire recurrence intervals. Caribou
nutritional status and population dynamics may be incorporated into the
model where appropriate.

This model will provide managers a tool to evaluate the abundance of
caribou forage lichens expected under various fire recurrence regimes.
In addition, because of the spatially explicit structure, it allows
evaluation of variation in fire recurrence among specific portions of
the NCH range. Because it is a time-series model, it allows evaluation
of shorter-term temporal effects of changes in fire recurrence.

##### Roles and Responsibilities of Investigators

Tom McCabe and Lisa Saperstein, formerly with the USGS, Alaska
Biological Science Center, developed the original proposal for this
study. Significant portions of their effort have carried forward to this
study plan.

**USGS-ALASKA BIOLOGICAL SCIENCE CENTER**

**Layne Adams** will serve as project director and will be responsible
for the overall coordination and conduct of this research program. He
will participate in fieldwork, data analysis, and report writing as
needed. He will be responsible for reviewing reports and other products
produced as part of this research and ensuring their timely submission
to funding agencies.

**Kyle Joly** will be responsible for acquiring or developing necessary
GIS coverages, analyzing remotely sensed data, and managing and
analyzing GPS-telemetry and conventional radiotelemetry data. He will
assist with all project fieldwork. He will be responsible for developing
an annotated bibliography and serve as senior author on manuscripts on
habitat selection and factors influencing movements and distribution of
caribou.

**ALASKA DEPARTMENT OF FISH AND GAME**

**Bill Collins** will be responsible for vegetation analysis aspects of
this research program and for developing manuscripts on these topics. He
will be responsible for conducting related fieldwork, and will assist
with other project fieldwork.

**Bruce Dale** will serve as principal investigator responsible for
integrating all components of the research program. He will have primary
responsibility for caribou capture and radiotelemetry related fieldwork
and assist with other fieldwork. He will be responsible for developing
progress reports and the final report with appropriate assistance from
other project personnel. He will have senior authorship responsibility
on all aspects of the project not previously specified.

##### Reports

Annual progress reports will be provided to all cooperating agencies by
1 March 2001-2003. Progress reports will include a summary of research
activities and significant accomplishments during the previous year, and
a description of research activities planned for the upcoming year.

A final report will be provided to all cooperating agencies by 30
September 2004. The final report will include an overview of research
activities, synthesis and interpretation of research results, a
discussion of management implications of the completed work, and an
annotated bibliography.

Manuscripts for publication will be developed as a result of this
research. Project collaborators will share authorship.

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##### Schedule

Schedules are presented by the federal fiscal year commencing 1 October.
State of Alaska fiscal years commence 1 July.

**FY99 ‑ Initiate field work**

Deploy conventional collars

Monitor group size, movements, and distribution

Evaluate nutritional performance of caribou

**FY00 -- Development of Study Plan and Field Research**

Finalize study plan

Develop GIS coverages

Deploy GPS and conventional collars

Monitor group size, movements, and distribution

Evaluate nutritional performance of caribou

Evaluate lichen abundance and distribution

**FY01 ‑ Field Research**

Analyze field data collected

Deploy GPS and conventional collars

Monitor group size, movements, and distribution

Evaluate nutritional performance of caribou

Evaluate lichen abundance and distribution

Annual Progress Summary

**FY02 ‑ Field Research**

Analyze field data collected

Deploy GPS and conventional collars

Monitor group size, movements, and distribution

Evaluate nutritional performance of caribou

Evaluate lichen abundance and distribution

Annual Progress Summary

**FY03 ‑ Field Research, Data Analysis, Manuscript Preparation**

Analyze field data collected

Deploy GPS and conventional collars

Monitor group size, movements, and distribution

Evaluate nutritional performance of caribou

Annual Progress Summary

**FY04 -- Data Analysis, Field Research, Manuscript Preparation**

Analyze field data collected

Evaluate nutritional performance of caribou

Prepare Final Report