Scott Rupp

A Geographic Model of Landscape-Level Post-Disturbance Forest Establishment Patterns of Interior Alaska White Spruce Ecosystems

A geographic model of early post-disturbance forest establishment patterns, Alaskan Boreal Forest Establishment Model (ABFEM), is described for white spruce ecosystems in interior Alaska. The model has been created within the GRID environment of ArcInfo, utilizing a complex set of AML's. ABFEM simulates the production of seed, dispersal of seed, disturbance effects upon the seedbed, vegetative reproduction potential, and the early establishment patterns of tree seedlings within a burnt upland white spruce ecosystem. The spatially explicit model can simulate regeneration dynamics upon a defined landscape unit. The model realistically simulated seed dispersal and seedling establishment patterns of white spruce following the 1983 Rosie Creek fire, 20 km southwest of Fairbanks. An example of application of the model to forest management is presented.

Introduction

Natural tree regeneration within a disturbed forest landscape is an important component of sustainable ecosystem management. The ability to predict and describe early post-disturbance seedling establishment patterns offers important management information. Information regarding the potential for natural regeneration can describe the recolonization of a disturbed site and identify areas needing special attention. A geographic model that can simulate the establishment of seedlings on a disturbed landscape and identify problem areas that may not regenerate naturally offers an additional tool for forest management.

Interior Alaska is characterized by a mosaic of vegetation, which reflects the disturbance history of the landscape (Viereck 1973). In the uplands, fire resets the successional clock (Van Cleve et al. 1991). Fire frequency for interior white spruce ecosystems is estimated at 105 yr (Yarie 1981). These white spruce ecosystems are both commercially and socially important components of the boreal forest. The wise management and continued persistence of these systems are important issues in the management of interior Alaska forests. Simulating early establishment patterns of white spruce can provide information helpful in the management of these systems and may provide for a decreased dependence upon planting of both burned and harvested sites.

A geographic information system (GIS) provides an excellent framework for developing a spatially explicit model of early post-disturbance seedling establishment patterns. The strict geographic representation allows the identification of specific seed sources, description of the seedbed, and simulation of the effects of competing vegetation and environmental constraints upon seedling establishment. Creation of the model within a GIS provides for spatially explicit representation of key biological processes and offers the land manager relevant information on the regeneration patterns of a defined landscape.

The objective of this study was to develop a geographic model of white spruce seedling establishment patterns within an upland white spruce community following a wildfire event. The model was developed entirely within ArcInfo using AML's and the GRID environment. Routines were developed to simulate the production and dispersal of seed upon the landscape, to simulate the effects of fire upon the seedbed and competing vegetation, and simulate the initial establishment pattern of seedlings within a burnt upland white spruce ecosystem.

This paper provides a general description of the model and details individual model routines. A brief comparison of model results against both field observations and the literature is included. The discussion focuses on potential management applications.

The Model

Concept

The Alaskan Boreal Forest Establishment Model (ABFEM) is a geographic model of forest tree seedling establishment. The model is currently restricted to burnt upland white spruce ecosystems of interior Alaska. ABFEM focuses on the reproduction of white spruce by seed. ABFEM follows a generalized view of the conceptual model of natural regeneration from seed by Harper (1977), which has three main components: (i) the production of seed, (ii) the seed bank, including the current seed crop, and (iii) the environmental sieve comprising all biotic and abiotic factors affecting germination, growth, and survival.

The model subroutines are a simplistic representation of Harper's model (1977), focusing on the simulation of white spruce seedling establishment patterns. ABFEM simulates the production of seed (white spruce and paper birch), the reproductive potential of the landscape including both the seed (white spruce and paper birch wind dispersed seed) and vegetative bank (paper birch basal sprouts and aspen root suckers), and the early establishment of seedlings (white spruce, paper birch, and aspen) conditioned by the environmental sieve described by Harper (1977).

Methods

ABFEM was created within the GRID environment of ArcInfo. Each routine runs entirely within ArcInfo utilizing a complex set of AML's. Menus allow the user to initialize input variables and parameters and provide the user with specific simulation options. Routines utilize grids and scalars; cell and scalar values represent parameter (input and output) values. All input and output is geographically referenced. A grid cell size of 10 meters (100 m^2 ) is used.

Empirical data used to develop and calibrate individual routines were measured in the Fairbanks area of interior Alaska over the past 4 decades. Data from the literature was used to fill certain information gaps. Model validation was carried out through comparison of model predictions with the literature and observations from interior Alaska.

Seed Production

The routine SEEDS simulates the annual production of total and viable seed for white spruce and paper birch source stands. Cone/catkin and seed production is simulated using a simple Monte Carlo technique following the work of Fox et al. (1984). The model utilizes a pseudo random number generator AML, probabilities, and rules to simulate the probabilistic nature of cone/catkin and seed production for interior Alaska. The user has the option of defining seed production values, which may be required for historical reconstruction or when a specified production level is to be simulated.

Using probabilities and random numbers, the cone/catkin crop is rated as poor, moderate-good, or excellent. The total within stand seed fall per unit area (100 m^2 ) and percent viability associated with the cone/catkin crop rating are calculated. The NORMAL function in GRID is used to develop normal distributions following the production trends observed from long-term records of seed production, in the Fairbanks area of interior Alaska. NORMAL develops a normal distribution and then randomly choses a value within the distribution for output. A normal distribution of total seed density is developed based upon the observed means and standard deviations from local production records for the given crop rating. A value within the distribution is randomly chosen and assigned to the parameters of total seed and percent viable. Total within stand viable seed is then calculated. The routine output is the number of viable seed per unit area (100 m^2 ) produced within the source stand for each species.

Seed Dispersal

The routine DISPERSE disperses white spruce and paper birch seeds from the source stand(s) onto the disturbed landscape. The general seed dispersal pattern of forest trees follows the negative exponential model (Okubo and Levin 1989, Willson 1992, Farmer 1997). Information from the literature and observations from interior Alaska for white spruce (Rowe 1955, Schlesinger 1970, Dobbs 1976, Zasada and Lovig 1983, Zasada 1985, Youngblood and Max 1992, Greene and Johnson 1995, Rupp personal communication) and paper birch (Bjorkbom et al. 1965, Bjorkbom 1971, Zasada 1985, Safford et al. 1990) were used to develop general dispersal curves for each species following the negative exponential model.

DISPERSE calculates the Euclidean distance of each cell within the disturbed area to the closest source cell, using the EUCDISTANCE function. This distance grid is input into the dispersal equation along with the viable seed parameter to create an output grid of dispersed viable seed.

Reports from the literature suggest the influence of winds upon the "seed shadow" (Harris 1967, Schlesinger 1970, Janzen 1971, Sharpe and Fields 1982, Zasada and Lovig 1983, Youngblood and Max 1992, Greene and Johnson 1995, 1996, Rupp personal communication). The WINDS subroutine provides the option for the simulation of potential seed dispersing wind effects upon the spatial distribution of white spruce seed about its source. The euclidean direction of each cell within the disturbed area to the closest source cell is calculated and used to determine the proper dispersal equation to be applied. Separate dispersal curves were developed for white spruce seed dispersing with the wind and seed dispersing against the wind.

Seedling Establishment

The ESTABLISH routine simulates the early post-disturbance establishment patterns of seedlings (white spruce, paper birch, and aspen) upon the landscape. The routine implicitly simulates the various biotic and abiotic factors which determine the germination, survival, growth, and initial establishment of a seedling. The routine outputs the spatial distribution of established seedlings, an established seedling is defined as a 5 year old seedling, resulting from the specified seed crop, vegetative reproduction, and seedbed conditions encountered. Several subroutines are accessed by ESTABLISH to produce output grids of established seedlings.

ESTABLISH uses a dynamic seed:seedling index and seedbed information along with the grid of dispersed seed to determine successfully established seedlings on a cell by cell basis. The dynamic seed:seedling index is an index that calculates the number of viable seed needed to produce one established 5 year old seedling given the time since disturbance and the seedbed conditions encountered by the dispersed seed. Seedlings originating from seed (white spruce and paper birch) are calculated using the seed:seedling index. The seed:seedling index uses seed:seedling ratios developed for white spruce (Ackerman 1957, Eis 1967, Zasada 1971, Clautice 1974, Ganns 1977, Zasada et al. 1978, Walker 1986, Walker et al. 1986, Zasada et al. 1992, Zasada personal communication) and paper birch (Marquis et al. 1964, Horsley and Abbott 1970, Zasada 1971, Clautice 1974, Zasada et al. 1978, Zasada 1985, Perala 1987, Perala and Alm 1989) associated with seedbed substrate characteristics, topography, and time since disturbance to determine the number of 5 yr seedlings established within a cell.

Seedbed substrate characteristics are simulated by the subroutines DISTURB and SPROUT. The DISTURB subroutine simulates the effects of a disturbance (wildfire) upon the forest floor and subsequent seedbed. Fire in interior Alaska, as well as in most fire-dominated systems, is heterogeneous in nature; producing a spatially variable pattern in both areal extent and intensity/severity (Lutz 1956, Quirk and Sykes 1971, Viereck 1973, Heinselman 1981, Thomas and Wein 1985, Hobbs and Atkins 1988, Ratz 1995). DISTURB produces a heterogeneous burn pattern across the landscape, resulting in various degrees of forest floor consumption. A grid of random numbers is produced for the disturbed area and classified into 1 of 5 burn severity classes, following Dyrness and Norum (1983). The probability of occurrence of a specific severity class is input by the user. Several heterogeneous burn patterns can be defined by the user, which are created using the initial classified disturbance grid and various focal functions (i.e. FOCALMEAN and FOCALMAJORITY) within GRID.

The subroutine SPROUT simulates the occupation of disturbed cells by aspen root suckers and paper birch basal sprouts. This subroutine provides information on both potential establishment patterns of aspen clones and paper birch sprouts and provides information on specific vegetative competition, in terms of site occupation, experienced by white spruce seedlings. SPROUT uses pre-disturbance vegetation patterns and output from DISTURB to determine post-disturbance vegetative reproduction patterns.

Problems

The major problem involves information gaps and lack of data for critical processes simulated by the model. Although four decades of research have been conducted in interior Alaska, we still have many questions to answer involving issues of early regeneration dynamics. From a research standpoint, the model has identified critical information gaps and serves as a tool for hypothesis development.

From a functional standpoint, each model routine has some major shortcomings. SEEDS performs well simulating general trends in production periodicity and variability. However, the routine lacks the predictive ability to identify actual occurrence of future crops, important to natural regeneration management.

DISPERSE describes the general pattern of seed dispersal for white spruce and paper birch and provides some indication of the influence winds may have upon the seed shadow. However, more information is needed to accurately model dispersion of seed upon the landscape and identify specific effects of topography and wind upon the spatial distribution of seed. For example, research regarding aspen regeneration has focused on vegetative reproduction (Peterson and Peterson 1992) and little information exists on aspen seed dispersal. Also, certain irregular seed source shapes result in discontinuity of the seed shadow, due to the manner in which the euclidean direction of each cell is calculated by the EUCDISTANCE function in GRID.

The ESTABLISH routine is a very simplistic representation of the establishment process and over-generalizes the complexity of the germination, survival, growth, and establishment of seedlings. This approach was intentional, with the objective of describing seedling establishment patterns in a manner that would provide practical information to the land manager. Seed:seedling ratios provide this practicality, but the data needed to develop the proper level of detail, in terms of viable seed needed to produce an established seedling for a specific seedbed environment, is lacking. Furthermore, representation of the spatial pattern of fire effects by DISTURB lacks empirical data. SPROUT also lacks empirical data, particularly for interior Alaska, and provides only a speculative generalized view of the vegetative reproduction process in burnt upland white spruce communities in interior Alaska.

Model Results

Comparison to Field Data

The model was run to simulate the observed establishment patterns of white spruce following the Rosie Creek fire. In 1983 the Rosie Creek fire burned 3,482 ha, including one-third of the Bonanza Creek Experimental Forest (BCEF), located approximately 20 km southwest of Fairbanks (Juday and Dyrness 1985). The fire burned through upland stands of white spruce and mixed hardwoods, leaving some stands unburned. BCEF has been the site of extensive forest research since the late 1950's and continues currently as part of the BNZ/CPCW Long-term Ecological Research (LTER) site. The long-term record of seed production was used to simulate seed production levels and observations of actual post-fire establishment patterns were used for comparison with model predictions.

The simulation involved an area of the BCEF uplands containing stands of pure white spruce and white spruce/mixed hardwoods. A 35 ha island of white spruce/mixed hardwoods survived the fire and served as a major seed source (Figure 1).

Figure 1 - Color infrared image of a portion of the Bonanza Creek Experimental Forest and the Rosie Creek burn, showing the 35 ha island of white spruce/mixed hardwoods that survived the 1983 fire.

The model predicted similar trends of white spruce seedling establishment to those actually observed within a 1 ha portion of the burn (Figure 2).

Comparison of Observed and Predicted Seedling Establishment Patterns

Figure 2 - Comparison of the observed and predicted spatial distribution of the 1983 and 1987 white spruce seedling cohorts, which established after the Rosie Creek fire at BCEF.

The model predicted seedling densities for the 1983 cohort significantly higher than actually observed. The predicted densities for the 1987 cohort followed closely to those observed. The model predictions for both cohorts showed a strong correlation, 0.79 and 0.73 for the 1983 and 1987 cohorts respectively, to observed trends between seedling density and distance from the source stand.

Comparison to the Literature

A review of the literature identifies few comparable studies involving the natural post-disturbance establishment patterns of white spruce communities in interior Alaska. Reports of white spruce establishment on disturbed sites in interior Alaska show a large range in observed density levels. (Zasada et al. 1978, Zasada and Grigal 1978, Zasada 1985, Wurtz and Zasada 1987, Packee 1990, Zasada personal communication). Zasada (1985) working within the Rosie Creek burn observed one year old seedling densities ranging from 0 to 64800 seedlings per 100 m2. Zasada (personal communication) working in a clearcut with scalped and unscalped surfaces observed five year old seedling densities ranging from 0 to 11000 seedlings per 100 m2. Zasada et al. (1978) reported a five year old average seedling density level of 3115 seedlings per 100 m2. ABFEM predicted individual cell values for the entire Rosie Creek burn ranging from 0 to 242700 and 0 to 67200 five year old seedlings per 100 m2, for the 1983 and 1987 cohorts respectively.

Other regions of the North American boreal forest provide for only a very generalized comparison, due to significant differences in both site and environmental conditions. Studies from Canada report a range of seedling densities from 0 to over 9000 seedlings per 100 m2 on disturbed sites (Phelps 1948, Timoney and Peterson 1996).

Discussion

Management Applications

ABFEM provides geographically explicit information that can assist the land manager in management decisions and aide in the development of harvest layouts. The model provides output of potential use for several management issues including: identifying areas that will not regenerate, maximizing the area of potential natural regeneration success, and identifying areas needing specific site preparation.

The BCEF study site can be used to demonstrate potential management applications. Using the island of surviving white spruce/mixed hardwoods (Figure 1), we can provide an example of the potential use of ABFEM in silvicultural planning and decision making. The island serves as a hypothetical seed source following a harvest operation (i.e. clearcut, strip cutting, etc.). ABFEM allows for investigation of the influence of seed source orientation and its interaction with wind upon the seed shadow. Figure 3 provides an example of the influence seed source orientation can have upon the seed shadow of white spruce.

Figure 3 - Example of seed source orientation and wind influences on the seed shadow of white spruce. The simulation was run using 1987 within stand seed production levels (3743 viable seeds per m^2) at BCEF.

Figure 3a, where the long axis of the seed source is perpendicular to seed dispersing winds, maximizes the dispersion of seed upon the landscape. In figure 3b, where the long axis is parallel to the winds, dispersion is ineffective.

The model can also be utilized to investigate the potential influence of site preparation upon seedling establishment patterns. Figure 4 provides an example of the influence seedbed preparation can have upon establishment patterns.

Figure 4 - Example of seedbed preparation influence upon white spruce seedling establishment. The simulation utilized the dispersed viable seed grid from fig. 3a.

Figure 4a shows seedling establishment patterns following scarification of the site, producing mineral soil seedbed conditions. Scarification shows a potential positive affect upon natural establishment patterns of white spruce seedlings. Figure 4b, where no site preparation was performed and the seedbed was assumed to be heterogeneous in nature, shows a reduction in both density and areal extent of established seedlings compared to figure 4a. Figure 4, besides identifying natural regeneration patterns, also identifies areas that will not be adequately regenerated naturally. This could allow the land manager to concentrate planting efforts at those locations, maximizing both economic and operational efficiency.

Conclusions and Future Research

ABFEM realistically simulates general upland white spruce seedling establishment patterns following wildfire in interior Alaska. The model provides for the simulation of seed production, seed dispersal, disturbance effects upon the seedbed, vegetative reproduction potential, and the early establishment patterns of tree seedlings within a burnt white spruce ecosystem. The spatially explicit nature of the model allows the user to investigate various scenarios of seed availability and seedbed receptivity and the combined effects they have upon seedling establishment patterns upon the landscape. The potential application of the model in forest management is promising, providing the land manager with basic information important in the development of a successful silvicultural plan.

The model has several shortcomings related to the lack of empirical data for key processes and due to limitations in the GRID environment and AML. Future research will concentrate on gathering the empirical data needed to properly parameterize and calibrate the model and to further develop the model structure towards a user friendly format for use by the land manager.

Acknowledgements

This research was funded by the National Science Foundation grant DEB-9211769 (Taiga Long-Term Ecolocigal Research Program).

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Scott Rupp
Doctoral Candidate
Forest Soils Laboratory
University of Alaska
Fairbanks, AK 99712
Telephone:(907) 474-7019
FAX: (907) 474-6184

srupp@salrm.alaska.edu