Ecological Modelling 142 (2001) 261– 283
www.elsevier.com/locate/ecolmodel
Simulating the effects of different fire regimes on plant
functional groups in Southern California
Janet Franklin a,*, Alexandra D. Syphard a, David J. Mladenoff b,
Hong S. He c, Dena K. Simons a, Ross P. Martin a, Douglas Deutschman d,
John F. O’Leary a
b
a
Department of Geography, San Diego State Uni6ersity, San Diego, CA 92182 -4493 USA
Department of Forest Ecology and Management, Uni6ersity of Wisconsin-Madison, 1630 Linden Dri6e, Madison, WI 53706 USA
c
School of Natural Resources, Uni6ersity of Missouri-Columbia, 203M ABNR Building, Columbia, MO 65211 USA
d
Department of Biology, San Diego State Uni6ersity, San Diego, CA 92182 -4614, USA
Received 4 April 2000; received in revised form 12 December 2000; accepted 14 March 2001
Abstract
A spatially explicit landscape model of disturbance and vegetation succession, LANDIS, was used to examine the
effect of fire regime on landscape patterns of functional group dominance in the shrublands and forests of the
southern California foothills and mountains. Three model treatments, frequent (35 year), moderate (70 year), and
infrequent (1050 year) fire cycles, were applied to the landscape for 500 year. The model was calibrated and tested
using a dataset representing an initial random distribution of six plant functional groups on an even-aged landscape.
Calibration of the three fire regime treatments resulted in simulation of fire cycles within 7% of these intended values
when fire cycles were averaged across ten replicated model runs per treatment. Within individual 500-year model runs,
the error in the simulated fire cycle (average area burned per decade) reached 11% for the moderate and frequent fire
cycle treatments and 53% for infrequent. The infrequent fire regime resulted in an old landscape dominated by the
three most shade tolerant and long-lived functional groups, while shorter-lived and less shade tolerant seeders and
resprouters disappeared from the landscape. The moderate fire regime, similar to what is considered the current fire
regime in the southern California foothills, resulted in a younger landscape where the facultative resprouter persisted
along with the long-lived shade tolerant functional groups, but the obligate seeder with low fire tolerance disappeared,
despite its moderate shade tolerance. The frequent fire regime resulted in the persistence of all functional groups on
the landscape with more even cover, but the same rank order as under the moderate regime. The model, originally
developed for northern temperate forests, appears to be useful for simulating the disturbance regime in this fire-prone
Mediterranean-type ecosystem. © 2001 Elsevier Science B.V. All rights reserved.
Keywords: Chaparral; Disturbance; Fire; Mediterranean-type ecosystem; Succession; Spatially explicit landscape model
* Corresponding author. fax: + 1-619-594-4938.
E-mail address: janet.franklin@sdsu.edu (J. Franklin).
0304-3800/01/$ - see front matter © 2001 Elsevier Science B.V. All rights reserved.
PII: S 0 3 0 4 - 3 8 0 0 ( 0 1 ) 0 0 2 8 6 - 1
262
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
1. Introduction
Climate, land use, and resource management
affect the disturbance regimes that shape ecosystem structure and function (Torn and Fried, 1992;
Westman and Malanson, 1992; Baker, 1995;
Davis and Michaelsen, 1995; Malanson and
O’Leary, 1995; Suffling, 1995; Gardner et al.,
1996; Larsen, 1997; Flannigan et al., 1998). A
disturbance regime such as fire or flooding is
characterized by frequency, size, seasonal timing
and intensity (Godron and Forman, 1983; Pickett
and White, 1985; Pickett et al., 1989; Johnson and
Gutsell, 1994). While much of the literature on
environmental change focuses on future (anthropogenic) climate and land use change (Meyer and
Turner, 1994; Houghton, 1997), the fire regimes in
the world’s Mediterranean-type ecosystems have
been affected by centuries to millennia of landscape alteration due to intensive and extensive
land use, and deliberate use and suppression of
fire (Naveh and Dan, 1973; Naveh, 1975; Moreno
and Oechel, 1992, 1994, 1995; Pausas, 1999a).
Within the last century the shrub- and forestdominated Mediterranean-type ecosystems of
southern California’s foothills and mountains
have been subjected to changing patterns and
intensities of land use, as well as policies regarding fire suppression and management (Minnich,
1983, 1991a,b; Chou et al., 1993; Minnich et al.,
1995; Keeley et al., 1999). For almost a century a
land management policy of fire suppression has
been in effect throughout most of the region
(Minnich, 1983; Keeley et al., 1999). Conversion
of shrubland and forest to agricultural and urban
land use alters the flammability of certain parts of
the landscape (e.g., converted to non-native grassland for grazing). It also affects the location,
frequency, and timing of human-caused ignition
(accidental and deliberate) in proximity to densely
settled areas (Keeley et al., 1999). These land
cover alterations can change vegetation composition via the effect of landscape fragmentation on
plant species dispersal and gene flow (Saunders et
al., 1991; Soule et al., 1991) and invasion by
non-native species (Alberts et al., 1993).
There is controversy in the literature about the
‘natural’ disturbance regime in the region, and the
effect of fire suppression on the fire regime. Zedler
(1995b) asserted that because climate, human
population, and land use and resource management practices have fluctuated widely over the
past 10 000 years, it is doubtful that the fire cycle
has been stable for more than a few decades or
centuries. Further, Keeley et al. (1989) noted that
certain chaparral species, those for whom seedling
recruitment is dependent upon fire, are threatened
if the time between fires is extremely short (less
than the time required for a plant to reach maturity). However, both authors also concluded that
the fire interval rarely exceeds the longevity of
these ‘obligate seeders’ (that can survive for a
century), and that chaparral communities are resilient to a wide range of fire return intervals (fire
cycles).
Minnich and his colleagues (Minnich and
Chou, 1997, and references therein) compared
chaparral-dominated landscapes in southern California, where fire suppression has been practiced
since ca. 1900, to northern Baja California, Mexico, where suppression has been very limited.
Their results suggested that the chaparral fire
cycle (the time required to burn an area equivalent to the area under consideration) is similar
( 70 year) in both regions because fire in
chaparral is fuel limited (there is an age-dependent combustion threshold). They concluded that
fire suppression in southern California has reduced fire frequency and increased fire size, restricting fires to extreme weather. They also
attributed the fine grained patch structure in Baja
to the lack of fire suppression there.
Keeley et al. (1999), on the other hand, found
no evidence for larger, less frequent fires resulting
from suppression during the 20th century in
southern California shrublands. Their analysis
showed that the average fire size decreased while
the number of fires increased (which they attributed to increased anthropogenic ignitions),
leading to a shorter return interval (on the order
of 30 –40 year) in the second half of the century.
Nor did they find evidence that fire in shrublands
was fuel limited (that younger age classes prevented fire spread), and therefore they concluded
that the greatest ecological threat from this increased fire frequency is the replacement of shrub-
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
lands by nonnative grasslands. Both Minnich’s
and Keeley’s analyses of shrublands were based
on relatively short (30– 80 year) air photo and
fire history records, respectively.
In contrast with the conflicting evidence regarding the effect of fire suppression on shrublands,
the story for southern California’s montane
forests is more straightforward. Research based
on dendrochronological reconstructions of fire
history, and repeat sampling of historical vegetation plots, indicates that fire suppression in the
montane conifer zone of southern California and
elsewhere in the western US has caused profound
changes in stand structure including increased
density of shade tolerant trees in the subcanopy
(Savage, 1991; Minnich et al., 1995; Stephenson
and Calcarone, 1999; reviewed in Keeley et al.,
1999 and Miller and Urban 2000a).
To explore the effects of different disturbance
regimes on vegetation patterns over long time
spans, models that are capable of explicitly simulating landscape-scale spatial processes over large
areas — fire spread and plant dispersal — are
effective tools (Mladenoff and Baker, 1999). A
previous model of southern California shrubland
succession as a function of disturbance regime,
climate change, and plant life history traits, did
not provide spatial predictions of landscape patterns (Malanson et al., 1992). Recent models of
patch dynamics (spatial patterns of landscape age
structure) as related to climate and fire regime
(Baker et al., 1991; Baker, 1995; Davis and
Michaelsen, 1995), as well as Markov models of
patch recovery (Turner et al., 1994; Gardner et
al., 1996), have not addressed succession or
changes in species composition. The FATE model
of Moore and Noble (1990), and a first-order
Markov model developed by Rego et al. (1993),
both simulate multiple pathways of succession as
a function of the timing and/or seasonality of
stochastic fire disturbances; however, these models are not spatially explicit. A non-spatial gaptype model was developed to examine effects of
fire on woody invasions of grasslands in Texas
(Fuhlendorf et al., 1996). Another gap model,
FM, was recently developed to address forest
responses to changes in the fire regime in the
southern Sierra Nevada (Miller and Urban,
263
1999a,b, 2000a,b). FM, derived from the ZELIG
model, simulates surface fires, and has certain
spatially explicit components.
Detailed models of fire spread (Rothermel,
1972; Albini, 1976) embody physical realism in
simulating fire behavior as a function of vegetation characteristics, meteorological conditions,
and topography, for discrete fire events (Kessell,
1979; Kessell et al., 1984; Kessell, 1990 and see
Wu et al., 1996). However, these models are not
usually capable of simulating long-term fire
regimes for large areas (Baker et al., 1991; Vasconcelos and Guertin, 1992; Davis and Burrows,
1994). On the other hand, landscape models have
been developed that use stochastic approaches to
simulate repeated disturbances. The majority of
these existing landscape models focus on one
landscape process, typically fire, assuming that
landscape processes can overwrite fine-scale vegetation dynamics (e.g., Green, 1989; Baker et al.,
1991; Gardner et al., 1996). Keane et al. (1997)
developed the FIRE-BGC model, linking a gap
model of succession to a mechanistic fire behavior
model, and simulated ecological and ecosystem
processes from the tree to the landscape scale.
This model is limited by intensive computation
and parameterization (more than a thousand
parameters) requirements.
LANDIS (LANdscape DIsturbance and Succession), a spatially explicit model, was designed
to represent a heterogeneous forested landscape
that is subject to disturbance events (fire,
windthrow and timber harvest) and to simulate
how these events influence plant succession over
decades or centuries (Mladenoff et al., 1996; He
and Mladenoff, 1999a,b; He et al., 1999a,b;
Mladenoff and He, 1999). LANDIS allows succession to take multiple pathways (Cattelino et
al., 1979; Noble and Slatyer, 1980; Vasconcelos
and Zeigler, 1993), and is based on the interaction
between species life history traits, site conditions,
and the disturbance or management regime on the
landscape.
The purpose of this research was to use the
LANDIS model to study the responses of key
dominant plant species in southern California
(modeled as functional groups) to different disturbance regimes. We conducted modeling experi-
264
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
ments to address the following research questions:
(a) Can LANDIS be calibrated to simulate a
specified fire regime for this landscape? (b) What
does the LANDIS model predict will be the effect
of long, medium, and short fire return intervals on
the extent and patch structure of the functional
groups and age classes on the landscape? (c) Are
the predicted patterns reasonable in light of what
is known about community dynamics in these
systems? LANDIS was developed and tested for
forests with longer fire return intervals (He and
Mladenoff, 1999a,b). Although the model effectively simulated these other forested ecosystems
(Gustafson et al., 2000; Shifley et al., 2000), we
wanted to test its ability to simulate fire regimes
characteristic of the southern California landscape. If LANDIS can produce logical predictions
using a highly simplified dataset, it gives us the
confidence to use it to explore alternative disturbance regimes with data representing more realistic landscape patterns of species composition and
site conditions.
2. The LANDIS model
The LANDIS model has been extensively described elsewhere (He and Mladenoff, 1999a,b;
He et al., 1999a,b; Mladenoff and He, 1999), and
will only be outlined briefly here. LANDIS implements a spatially explicit raster-based simulation
of the stochastically driven interactions between
plant life-history behaviors, site conditions, and
disturbance regimes. The vegetated landscape is
characterized by a map that contains (for each
grid cell) presence and absence information for
individual species (or functional groups) in 10year age cohorts. Multiple species and age cohorts
can be present within a cell. Environmental conditions associated with these species, i.e., conditions
that facilitate the establishment, growth, and relative dominance of a species, are approximated by
a landtype map. For each landtype, establishment
probabilities are assigned to each species, indicating the potential for a species’ successful establishment on that landtype (cf. Roberts, 1996).
Establishment probabilities should reflect what is
known about the environmental factors affecting
potential establishment and growth — the species’ response function or fundamental niche.
Each landtype is also characterized by a mean
fire-return interval, and a pattern of fuel accumulation and decomposition (potential fire severity)
over time, where fire severity is approximated by
an ordinal variable (1– 5).
When exposed to different fire return intervals a
plant community may demonstrate different seral
stages. This process, referred to as ‘multiple pathways of succession,’ results from differing species’
life-history attributes (Cattelino et al., 1979; Noble and Slatyer, 1980; Moore and Noble, 1990;
Noble and Gitay, 1996; Pausas, 1999a,b). Because
LANDIS utilizes life-history information, the
model can simulate the effects of different fire
regimes on succession. Each species in the model
is associated with life history parameters including
longevity, age of first reproduction, potential seed
dispersal distance, ability to resprout, shade tolerance, and tolerance of fires of varying severity
(the last two are ordinal variables ranging from
1 – 5).
In LANDIS, the probability of fire ignition
occurring in a grid cell is spatially stochastic, but
increases with the time since the last fire at a rate
determined by the fire-return interval for each
landtype. Fire size is also stochastic, but related to
a specified mean and log-normal distribution
function with small fires occurring more frequently than large fires. Successional dynamics
are simulated by growth, death, dispersal, establishment, and competition of species in burned
and unburned cells in each time step. Fire is
treated as a ‘bottom up’ process. In other words,
when an ignition occurs, or fire spreads from
another cell, the fire severity is determined by the
time since the last fire. Fire-induced mortality
depends upon the age-dependent fire tolerance of
the species (e.g. a fire of severity class 2 will kill all
but the oldest cohort of a species of fire tolerance
2).
Dispersal of any species to any cell in each time
step can occur if there is a source of propagules
within the dispersal distance for that species. Species establishment occurs as a function of the
probability of establishment on the landtype as
well as the shade tolerance of the potentially
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
establishing species relative to the age-dependent
shade tolerance of the species already present in a
cell. For example, a species of shade tolerance 2
might establish on a landtype for which it has a
high probability of establishment if there is a
species of shade tolerance 3 already on the site but
only in very young age classes. All of these processes (ignition, fire spread, dispersal, establishment, mortality) are stochastic. Thus, LANDIS
simulates dispersal and succession, even in the
absence of disturbance, and it simulates differential mortality of species age cohorts as a function
of fire severity and the time between fires.
In its temporal and spatial capacity, LANDIS
is semi-explicit because it uses discrete time units
of 10 years per iteration to record the occurrence
of fire and the age of the species cohorts in each
grid cell. Therefore, neither the age of individual
plants nor the occurrence of single fires is simulated by the model. Rather, groups of individual
fires (collectively referred to as a ‘fire entity’) can
occur in a time step, and species are treated as
present or absent in age cohorts within grid cells.
These cohorts subsequently age by decades as the
simulation progresses (growth is not simulated
explicitly in terms of biomass accumulation or
relative dominance). This approach reduces the
complexity of the data needed to run the model
and subsequently allows broad-scale, long-term
simulations over hundreds of years and millions
of hectares (He and Mladenoff, 1999a) while
maintaining attention to certain aspects of fine
grain detail.
3. Materials and methods
There are multiple dominant plant species in
southern California’s fire-prone vegetation types
(Barbour and Major, 1990; Gordon and White,
1994; Davis et al., 1995; Stephenson and Calcarone, 1999). Post-fire succession in these shrub
and forest communities is strongly tied to the life
history traits parameterized in LANDIS (Zedler,
1981, 1995a,b; Keeley, 1986, 1991a, 1995; Keeley
and Keeley, 1984; Keeley et al., 1989; Thorne,
1990; Zedler and Zammit, 1989; and references
therein). Because the effect of any fire-regime
265
upon a given community is strongly tied to the
varying life-history responses of the species found
there, it has been proposed that most if not all
species should be considered in simulating the
community’s dynamics (Zedler, 1995a). However,
it is the goal of modeling to incorporate only the
essential detail required to reproduce observed
patterns in an ecological system, and not to attempt to capture all of the details of the system
(Levin, 1992). Therefore, in this study, a limited
number of functional groups (representing the
dominant life-history behaviors of the plant species in the study area) were modeled rather than
species (see also Pausas, 1999a,b). We developed a
dataset with key characteristics of plant communities in the southern California foothills and mountains. This dataset enabled us to define plant
functional groups, their life history attributes, and
alternative fire regime scenarios.
3.1. Functional groups and their attributes
A list of dominant species in the foothill and
montane plant communities of the Peninsular
Ranges in California was derived from quantitative community descriptions (Gordon and White,
1994). A literature review of the life-history traits
of these dominant species allowed a profile of the
most common life-history strategies to be
matched with the functional group classification
of Noble and Slatyer (1980). Functional groups
were established by determining four ‘vital attributes’ (from Noble and Slatyer, 1980; Noble
and Gitay, 1996):
1. The method of arrival or persistence of
propagules at the site following a disturbance.
2. The conditions needed to establish and grow
to maturity.
3. The time required to reach critical life history
stages.
4. The size, growth rate, and mortality of the
species.
Because Noble and Slatyer (1980) consider the
first two attributes to be fundamental to describing the successional role of species, we used these
to define our functional groups (see Table 1). The
third and fourth attributes were then defined as
parameters of those functional groups (Table 2).
266
Table 1
Functional Groups life history strategies and disturbance response of dominant species in the southern California mountains and foothills, classified by the first two
vital attributes of Noble and Gitay (1996): method of persistence and conditions for establishment
Methods of persistence
W (Withstands
disturbance if mature)
V (Vegetative resprouting) S (Persistent
Seed Pool)
I (Shade-intolerant, can
only establish after
disturbance)
T (Can establish after
disturbance and as
canopy closes)
R (Requires a canopy to
establish, shade-tolerant,
late successional)
Fire Tolerant: Pinus jeffreyi
(Minnich, 1988, 1991a,b;
Vander Wall, 1993); Quercus
agrifolia (Keeley, 1977;
Calloway and Davis, 1993)
Facultative Resprouter:
Adenostoma fasciculatum
(Barro and Conard, 1991;
Hanes, 1971; Hilbert and
Larigaurderie, 1990;
Keeley, 1981, 1986,
1991a, 1995; Keeley et al.,
1989; Parker and Kelly,
1989; Zedler et al., 1983;
Zedler, 1995b)
Obligate Resprouter:
Quercus berberidifolia
(Hanes, 1971; Keeley,
1981, 1986, 1991a; Zedler,
1995b)
C (Short-lived seed pool)
D (Dispersers, propagules
available all sites)
Obligate Seeder A:
Ceanothus greggii (Keeley,
1977, 1986; Lariguaderie et
al., 1990; Bullock, 1991;
Zammit and Zedler, 1992;
Zedler, 1995b);
Arctostaphylous glauca
(Keeley, 1977; Fulton and
Carpenter, 1979; Zedler et
al., 1983)
Pioneer: Lotus scoparius
(Hanes, 1971; Haidinger
and Keeley, 1993; Keeley,
1995); Artemisia
californica (Hanes, 1971;
Zedler, 1981, 1982; Zedler
et al., 1983; Malanson
and Westman, 1985;
Keeley, 1995)
Obligate Seeder B:
Adenostoma fasciculatum
(seeding subpopulation)
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
Conditions for
Establishment
Parameter (units)
Longevity (year)
Age of maturity (year)
Shade tolerancea
Fire tolerancea
Effective seed dispersal distance (m)
Maximum seed dispersal distance (m)
Probability of vegetative propagation (0–1)
Minimum age of resprouting (year)
Reclass coefficientc (0–1)
a
Functional Groups
Pioneer
Obligate
SeederA
Obligate SeederB Facultative Resprouter
Fire Tolerant
Obligate Resprouter
10
1
1
1
−1b
−1b
0
0
0.25
60
20
2
3
50
100
0
0
0.50
90
10
3
2
100
200
0
0
0.50
300
50
4
5
100
750
0
0
1.0
120
30
5
4
500
1000
1
1
0.75
90
20
3
2
50
100
1
1
0.5
Parameter is represented by ordinal classes 1–5.
Dispersal distances of ‘−1’ in LANDIS mean that propagules are available at all locations at any time.
c
Reclass Coefficient (relative dominance of mature plant) is only used if output maps of species assemblages are produced.
b
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
Table 2
Species life history attributes used in LANDIS, and values of these parameters used for each Functional Group (see Table 1)
267
268
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
Although Noble and Gitay (1996) recognize several basic methods of persistence, only the following were used to define a set of functional groups
for our simulated area (Table 1):
W: The mature form of the species withstands
disturbance (e.g., thick-barked adults but vulnerable juveniles).
V: Juvenile or mature individuals survive the
disturbance but revert to a juvenile state (vegetative resprouting).
S: Persistence by seeds with long viability
stored in the soil.
C: Juveniles are only produced if adult was
present on site before disturbance. Seeds with
short viability often survive the disturbance
within protective fruits or cones, stored in the
canopy or soil.
D: Species with sufficient dispersal distances
that propagules are always available at all sites.
The following categories describe conditions required for establishment:
I: Shade-intolerant, early-successional species,
able to establish only immediately after a disturbance when competition is reduced.
T: Shade-tolerant, mid-successional species,
able to establish at any time, immediately after
disturbance but also as the canopy closes.
R: Shade-requiring, late-successional species,
usually unable to establish immediately after a
disturbance, but able to become established
once individuals of either the same species or
another species have established.
Based on the predominant post-disturbance recovery strategies of dominant species in the Peninsular Ranges, the following functional groups
were defined for the simulations: Fire Tolerant
(WT), Facultative Resprouter (VT), late-successional Obligate Resprouter (VR), early-successional Obligate Seeder A (CI), mid-successional
Obligate Seeder B, and Pioneer (DI) (Table 1). A
dataset was then developed with these functional
groups occurring in the map and parameter inputs
to LANDIS. The life history parameters for each
functional group are encoded in LANDIS’ species
attribute file (Table 2). These attributes were
derived from the literature (Table 1). In some
cases precise values for some of these attributes
have been published (e.g. Keeley, 1981). In other
cases only generalized or qualitative estimates
were available in the literature. Therefore, descriptions of these attributes were composited for
several species (Table 1) in order to parameterize
the functional groups (Table 2).
Because functional groups represent combined
behaviors of several species, the generalized attributes for these groups may not precisely fit a
particular species (Table 1). For example, in the
Pioneer functional group, Lotus scoparius is actually not widely dispersed but rather has a longlived seed bank with fire-stimulated germination,
and therefore may be better described as an S or
G species in Noble’s framework. On the other
hand, Artemesia californica has widely dispersed
seeds and is shade-intolerant, although it is not
usually thought of as a ‘Pioneer’ species within its
plant community. However, the dynamics of more
typical Pioneers such as fire-following native
ephemerals and non-native ‘weedy’ or ‘invasive’
annuals are not easy to capture with LANDIS’
current temporal resolution (10 year). In the Fire
Tolerant functional group, Pinus jeffreyi is not
‘shade tolerant’ per se, but is probably accurately
captured by Noble’s establishment type T, because it can establish after disturbance and also as
the canopy closes. Furthermore, LANDIS is not
yet able to truly simulate some of the methods of
persistence found in the study region, such as
fire-stimulated germination from a long- or shortlived seed pool. Therefore, Obligate Seeders A
and B must establish from seeds dispersed from
other cells following fire, and the performance of
these functional groups in response to fire will be
underestimated. Also note that we characterized
the widespread and dominant Adenostoma fasciculatum as two separate functional groups (pseudospecies) representing geographically distinct
subpopulations that predominantly regenerate either by seeding or by sprouting (Keeley and
Soderstrom, 1986).
A 250 ×250 cell map (where a cell represents a
50×50 m area — 62 500 cells or 15 625 ha) was
generated with an initial random distribution of
four map classes containing one- to two-species
assemblages of the functional groups (Fig. 1,
Table 3). The age distribution of the landscape
was initially set uniformly to 10 year (Fig. 1).
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
3.2. Landtypes and their attributes
Four landtypes were defined in order to capture
the elevation- and terrain-related gradients of temperature and available moisture occurring in the
study area. A map representing the spatial distribution of the landtypes was created by dividing the
map area into four equally-sized rectilinear sections
(Fig. 1) representing the north– south trending
elevational zones of the Peninsular Ranges, e.g., the
269
low elevation (warm, xeric) coastal sage scrub zone,
mid-elevation xeric (south facing slopes) dominated
by mixed chaparral, mid-elevation mesic (north
facing slopes) characterized by scrub oak
chaparral, and the high elevation (cool, mesic)
conifer forest zone (Table 3a). This map was
overlaid with the species and age maps, so that the
spatial input to LANDIS contained information on
species presence by age cohort, time since last fire,
and landtype class, for every cell (site).
Fig. 1. Landscape used in LANDIS simulations: initial random distribution of four map classes containing functional groups,
regular distribution of landtypes and uniform age distribution.
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
270
Table 3
(a) Functional group probability of establishment by landtype, and initial assignment of functional groups into four map classes (see
Fig. 1); (b) Fire severity curves (year fire severity class is reached)
(a)
Landtypes
Functional groups
Pioneer
Low elevation
Mid xeric
Mid mesic
High elevation
Functional group
map class
(b)
Landtypes
Low elevation
Mid xeric
Mid mesic
High elevation
1.0
0.7
0.3
0.2
1
Obligate
SeederA
0.5
1.0
0.5
0.2
2
Obligate SeederB Facultative
Resprouter
Fire Tolerant
0.2
0.5
0.3
0.1
2
0.1
0.3
0.5
0.2
3
0.2
0.3
0.7
1.0
4
Obligate
Resprouter
0.001
0.01
0.01
0.01
3
Fire severity class
1
2
3
4
5
0
20
20
30
–
30
30
50
10
50
50
80
20
80
80
130
30
130
130
210
The probability of establishment of each functional group on each landtype represents the functional groups’ ability to establish and grow under
different site conditions. A species’ fundamental
niche would be expected to be broader than the
realized niche (based on observed distributions),
with probabilities of establishment remaining high
on sites more favorable for establishment and
growth than the empirically observed ‘optimum.’
In a gap model of shrub communities in this
region, the fundamental niche was modeled by
assuming that precipitation greater than the optimum value would result in growth rates equal to
those at the optimum value, although no such
assumption was made for temperature (Malanson
et al., 1992). He et al. (1999b), using a forest gap
model, showed that species growth rates vary
across landtypes, and they are not highest on a
single landtype for all species.
In our simulated area, while we might expect
landtypes with high potential soil moisture and
mild temperatures to be most suitable for all
species, temperature and precipitation gradients
are confounded, and temperature is related to
both water stress and optimum temperatures for
growth. Although precipitation increases with elevation on the landtypes, species that find optimum conditions for growth and establishment on
the low elevation landtype would be limited by
minimum temperatures at high elevation. For
these reasons we parameterized the species establishment probabilities with values interpreted
from the literature (Table 3). The establishment
probabilities remained constant under all fireregime treatments. Because the map was initialized with a random distribution of functional
groups, we deliberately gave at least one functional group a high affinity for each landtype to
see how the model simulated the effect of dispersal and establishment. We parameterized the Obligate Resprouter as having a large dispersal
distance (Table 2), but extremely low probability
of establishment from seed on any landtype
(Table 3a), approximating the persistence method
of Quercus berberidifolia.
The fire severity curves, representing the temporal accumulation of fuel, are also controlled by
the landtype. Landtype fire severity classes are
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
shown in Table 3b. Xeric landtypes with slower
decomposition rates accumulate fuel more rapidly
than mesic sites (He and Mladenoff, 1999a).
3.3. Calibration and modeling experiment
In LANDIS, a fire regime is specified by the
mean return interval. The mean return interval is
the time required to completely burn an area
equivalent to a landtype and can be calculated
from the average area burned per time interval
(Johnson, 1992; Li et al., 1997). Calibration is
carried out by comparing the observed average
area burned, as well as the return interval for a
model run to specified values for those parameters. Therefore, to reduce the fire cycle error, fire
coefficients were systematically adjusted until observed approximated specified fire cycle. The burn
area coefficient, (a), is a scalar that relates the
simulated burn area to the specified value, where
disturbance sizes follow a lognormal distribution.
The fire probability coefficient, (b), scales the
probability of ignition to the specified return interval (He and Mladenoff, 1999a). Because the
modeling experiment consisted of three treatments
(fire cycles), each had to be calibrated separately.
Initially, default values of the fire regime parameters were used (a =50, b= 100). For each treatment first b, and then a, were systematically
varied, and the average area burned and return
271
interval were calculated, until no further improvements in the calibration were observed (10– 20
calibration runs per treatment). Final values of
these parameters were then used in the replicate
runs. Once each treatment was calibrated (using a
fixed random number seed), ten replicated 500year simulations (with varying random number
seeds) were performed for each treatment to examine the variability of model predictions.
The three simulated fire-regimes (Table 4) were
labeled Infrequent, Moderate and Frequent return
intervals. The Moderate and Frequent treatments
(Table 4) correspond roughly to estimated historical fire regimes reviewed Section 1, a 70-year
return interval (Minnich and Chou, 1997), and a
35-year return interval (Keeley et al., 1999) averaged across landtypes. A return interval of 100
and 50 year, respectively (Table 5), was specified
in these two treatments for the High-Elevation
landtype, dominated by the Fire Tolerant functional group. These values are less frequent than
values of 15– 50 year given in the literature for
conifer forests in southern California, northern
Baja California, and the southern Sierra Nevada
prior to fire suppression (Minnich et al., 1995). In
fact, because fire suppression has been so effective
in the conifer zone, it is estimated for the San
Bernardino Mountains that the fire regime of the
last 70 years would correspond to a 700 year
return interval (Minnich et al., 1995). Therefore,
Table 4
Calibration of fire cycle treatmentsa
Fire regime treatment
Expected area burned per decade, ha (%)
Mean observed area burned per decade, ha (%)b
S.D.
S.E.
Error
Range of error (ten replicates)
Expected return interval (year)
Mean observed return interval
Errorc
a
Infrequent
Moderate
Frequent
156 (1%)
160 (1%)
369 (2%)
17 (0%)
+3%
−32% to +53%
1050
975
−7%
2188 (14%)
2133 (14%)
1210 (12%)
86 (1%)
−3%
−11% to +9%
70
73
+5%
4688 (30%)
4778 (31%)
1625 (10%)
73 (0%)
+2%
−7% to +6%
35
33
−7%
The simulated area consisted of 250×250 (62 500) 50×50 m (0.25 ha) cells (15 625 ha).
Mean area burned and return interval is calculated from 50 time steps (500 years) × 10 iterations (replicates).
c
Error, expressed as percent, is [(Observed−Expected)/Expected] * 100.
b
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
272
Table 5
Simulated mean fire return intervals for landtypesa
Fire Regime
Landtype
Expected
Mean observed
Range
Infrequent
Low elevation
Mid xeric
Mid mesic
High elevation
Low elevation
Mid xeric
Mid mesic
High elevation
Low elevation
Mid xeric
Mid mesic
High elevation
600
900
1200
1500
40
60
80
100
20
30
40
50
536
806
1388
3622
32
97
120
227
12
49
79
175
388–823
512–1443
738–4264
1812–8768
27–35
89–108
112–131
185–261
11–14
47–49
76–85
168–183
Moderate
Frequent
a
Mean and range are based on ten replicate model runs.
the Infrequent regime was included to represent
this extreme condition of very effective fire suppression (a 1050 year return interval averaged
across the landscape).
Analyses comparing the effect of the three fire
regimes focused on the spatial location and extent
of the functional groups over time. Trends in the
age structure of the study area were examined to
determine whether the three fire-regime treatments created a landscape of predominantly old,
young, or mixed-aged patches. Patterns of cover
and age structure over time were analyzed with
the APACK (Analysis PACKage) software
(Mladenoff
and
DeZonia,
1999;
ftp://
flel.forest.wisc.edu/APACK/VERSION213/). Developed to analyze the spatial output of the
LANDIS model and other maps, APACK examines the model’s ERDAS 7.4 GIS output files for
each time step and can calculate many landscape
indices and metrics.
4. Results
4.1. Calibration
Error in the simulated area burned was +3 to
−3%, and in return interval was +5 to −7%,
for the three treatments (averaged for 50 time
steps and ten replicate runs; Table 4). The distribution of burned area per decade is shown (Fig.
2) for the replicate runs with the fire return interval closest to the specified value (averaging
burned area by decade over the ten replicates
would have been meaningless).
Despite this good overall fit between specified
and simulated fire cycle, the distribution of return
intervals across landtypes was often quite different than expected (Table 5). For each treatment,
the return interval increased across the four landtypes, as specified, but the return interval on the
low elevation landtype was always shorter than
specified, while it was longer than expected on the
mid-elevation mesic and high elevation landtypes.
The range of average return intervals across the
replicate model runs showed that the variability
was quite high for Infrequent (not surprising for a
500-year run and a 1050-year return interval) and
low for Frequent. Infrequent fire events generally
have greater variability than frequent events (He
and Mladenoff, 1999a). It was anticipated that the
model would simulate the unnatural fire regime
(Johnson, 1992) of a small average area burned
and a long fire-cycle (Infrequent) with high variability. Despite the high variability, under the
Infrequent treatment LANDIS effectively emulated conditions where fire is very infrequent relative to the other treatments.
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
273
4.2. Effect of fire regimes on the distribution of
functional groups on the landscape
To examine the effect of different fire regimes
on the trajectory of the functional groups, the
proportion of the study area occupied by each
functional group in each time step was summarized in output maps for each functional group
(Fig. 3). Table 6 summarizes the average propor-
Fig. 3. Relative (%) area of landscape covered by each functional group (Table 1) per time step (decade) for three fire
regime treatments: Infrequent, Moderate, and Frequent
(shown for the best-calibrated single model run).
Fig. 2. Relative (%) area of landscape burned per time step
(decade) for three fire regime treatments: Infrequent, Moderate, and Frequent (shown for the best-calibrated single model
run).
tion the landscape occupied by each functional
group for years 110– 500, after cover dynamics
had stabilized (Fig. 3). Fig. 4–6 show maps of
functional group presence/absence in year 500 for
each treatment.
Under the Infrequent fire regime, the landscape
experienced few fire events (Fig. 2). The functional groups demonstrated seral stages of landscape dominance in the first 100 years (Fig. 3).
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
274
Obligate Seeder B (early-maturing, a good disperser) covered the greatest extent of the landscape until the initial cohort began to senesce
(longevity is 60), and then, on average, covered
23% of the landscape (Table 6). After several
hundred years it was located on the landtypes for
which it has the greatest establishment probabilities (especially mid-elevation xeric), where patterns of seeding into burned patches from the
initial random distribution can be seen (Fig. 4).
The extent of the Pioneer functional group closely
tracked the area burned in all treatments, and
therefore was low and variable in this regime
(Table 6), and it was found on the low elevation
landtype (Fig. 4) that experienced the most fire
(Table 5). The Obligate Seeder A and Facultative
Resprouter declined rapidly as they reached the
age of senescence in this landscape without fire
(Fig. 3). These two functional groups declined to
very low levels and did not recover (Table 6)
because of their low shade tolerance, longevity,
and dispersal ability, relative to Obligate Seeder
B. The Fire Tolerant (WT) functional group expanded after it reached maturity (because it is
long-lived and shade tolerant). By the end of the
simulation the WT group was found preferentially
on the mid-elevation mesic and high elevation
landtypes (highest establishment probabilities),
both dispersing out from its initial random pattern, and in burned patches where it lacked competition from the Obligate Resprouter (Fig. 4).
The Obligate Resprouter expanded steadily
through the simulation (Fig. 3), because it was
parameterized to be shade requiring/tolerant.
Therefore, it can continually resprout in the cells
where it was initially placed, as well as expand its
cover slightly (Fig. 4), in spite of its low establishment probability, because of its good dispersal
ability and the lack of competition.
Under the Moderate regime, where burned area
is higher (Fig. 2), Pioneer flourished for 1– 2
decades following a fire (Fig. 3), located wherever
a fire had recently occurred, but typically at low
elevation (Fig. 5). Obligate Seeders A and B initially expanded in the absence of competition, but
declined precipitously after initial cohort senescence, similar to the response under the Infrequent treatment (Fig. 3; Table 6), and for the
same reasons (relatively short-lived, low shadetolerance). Because LANDIS cannot yet simulate
fire-cued germination from stored seeds after
adults are killed by fire on a site, a strategy
commonly found in ‘Obligate Seeders’ in these
shrub communities, these functional groups perform worse than would be expected under this fire
regime. The Facultative Resprouter maintained
higher cover under the Moderate than the Infrequent Regime (Fig. 3; Table 6), although it occurred on the landtype with the greatest fire
frequency, low elevation (Fig. 5), rather than on
the other landtypes where it had a higher probability of establishment (Table 3), but is faced with
greater competition. The Obligate Resprouter
showed much the same pattern as under the Infrequent regime for the reasons outlined in the discussion of that treatment. Fire Tolerant had
Table 6
Extent of map (cover) occupied by each functional group under each fire regimea
Functional group
Pioneer
Obligate SeederA
Obligate SeederB
Facultative Resprouter
Fire Tolerant
Obligate Resprouter
Infrequent
Moderate
Frequent
Mean
S.E.
CV
Mean
S.E.
CV
Mean
S.E.
CV
859
88
3618
440
6101
5797
18
3
29
26
63
31
41%
68%
16%
116%
21%
11%
2264
113
224
3433
9546
5567
69
16
11
16
27
28
61%
280%
95%
9%
6%
10%
3900
1319
844
3135
6516
5453
34
12
12
24
17
26
17%
18%
29%
15%
5%
10%
(6%)
(1%)
(23%)
(3%)
(39%)
(37%)
(14%)
(1%)
(1%)
(22%)
(61%)
(36%)
(25%)
(8%)
(5%)
(20%)
(42%)
(35%)
a
Mean in ha (and percent of map area), Standard Error (s.e.) and Coefficient of Variation (CV, standard deviation/mean, percent)
for 40 time steps (starting at year 110)×10 replicates.
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
275
Fig. 4. Map of each functional group at model year 500 for Infrequent fire regime (shown for the best-calibrated single model run).
greatest cover under this regime (Table 6) because
it is long-lived and shade- and fire-tolerant. It
dominated different landtypes proportional to its
establishment probabilities (Table 3), and the pattern of dispersal is obvious on landtypes where it
experienced little competition under this regime
(Fig. 5).
To achieve the return interval specified under
the Frequent regime, large areas of the landscape
burned in each time step (Fig. 2). The Pioneer
group completely covered the low elevation landtype (Fig. 6) which burned completely in almost
every time step (Fig. 2). As a result, it had low
temporal variability in cover (CV; Table 6). This
Frequent fire regime allowed Obligate Seeders A
and B to persist, although at low cover (Table 6;
Fig. 3), on the mid-elevation xeric land type (Fig.
6) where they had the highest establishment probabilities (Table 3). The Facultative Resprouter
had about the same average cover under this
276
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
regime as under Moderate (Table 6; Fig. 3), although it was distributed differently (Fig. 6). It
was found on the two landtypes with most frequent disturbance, low elevation and mid-elevation xeric, rather than only on the low elevation
landtype (as under the Moderate regime), or on
the landtype with the highest establishment probability (mid-elevation mesic), where it was usually
out-competed. Fire Tolerant had lower cover than
under the Moderate regime (Table 6), and was
concentrated on mid-elevation mesic and high
elevation landtypes (Fig. 6), where it had higher
establishment probabilities and experienced less
frequent fire. The Obligate Resprouter performed
much the same as before, for reasons already
discussed.
Fig. 5. Map of each functional group at model year 500 for Moderate fire regime (shown for the best-calibrated single model run).
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
277
Fig. 6. Map of each functional group at model year 500 for Frequent fire regime (shown for the best-calibrated single model run).
4.3. Age
Because of the infrequent occurrence of high
intensity fires, the Small-Infrequent regime permitted the landscape to grow old with a peak in
the 91 – 120 year age class (Fig. 7). The limiting
factor controlling maximum age was senescence
of the Fire Tolerant group. Moreover, because
this long-lived, shade-tolerant functional group
continued to recruit once it reached maturity, a
temporal gap in succession never occurred after a
cohort senesced unlike the other two regimes. Fire
regimes with Moderate to Frequent fire-cycles had
younger landscapes than the Infrequent regime
(Fig. 7). After several hundred years, the Moderate regime tended to produce a landscape where
most of the area was 0–180 years old, with a
small proportion of the landscape in older age
classes (181–300 year). The Frequent regime produced a more skewed landscape age distribution,
with most of the landscape in younger age classes
(0– 120 year) .
278
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
Fig. 7. Age distribution of landscape per time step for three fire regime treatments: Infrequent, Moderate, and Frequent (shown for
the best-calibrated single model run).
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
5. Discussion and conclusion
We were able to calibrate LANDIS to simulate
fire cycles within 7% of the specified values. This
indicates that the model, developed for forests
with longer return intervals, can be applied to the
southern California landscape with its shorter fire
cycles. Although the fit between observed and
expected fire cycle was good when averaged across
the entire simulated area the observed return interval was often shorter than specified on the low
elevation landtype, and longer than expected on
the mid- and high elevation landtypes. This discrepancy occurred because the simulated pattern
of fire results from the distribution of stand age
and fire severity class, and the availability of fuel
(age-dependent fire tolerance of species). Variability in simulated fire cycles was high, as expected,
for the Infrequent fire cycles, but low for Frequent because there are a limited number of ways
in which the simulated landscape can burn, given
the initial conditions, and achieve such a high
average proportion of burning for each time step.
Another purpose of the modeling experiment
was to determine if the effects of the different fire
regime treatments on the plant functional groups
were consistent with those described from shorterterm field studies of one or a few species. Under
all fire regimes, the dynamics of the Pioneer functional group, parameterized as a short-lived, well
dispersed, shade intolerant species, was closely
tied to fire events. Its average cover was directly
related to fire frequency and the area burned per
time step.
In contrast, the Fire Tolerant group was
parameterized to be extremely fire-tolerant, longlived, and able to recruit after disturbance as well
as between disturbances. Therefore, under the Infrequent regime, competition and succession controlled the overall landscape pattern of functional
group cover, with the Fire Tolerant group dominating after 110 year. After the original Fire
Tolerant cohorts began to senesce, this group was
outcompeted by the shade-tolerant and vegetatively reproducing Obligate Resprouter on certain
landtypes. The Fire Tolerant group also covered a
large portion of the landscape under the Moderate and Frequent fire regime, owing to its fire
tolerance, competitive ability and longevity.
279
The Facultative Resprouter was not maintained
on the landscape in the absence of fire (Infrequent), but was more successful when Moderate
or Frequent fire provided a ‘regeneration niche’
where it could propagate by post-fire resprouting.
The three moderately shade tolerant (mid-successional) functional groups, Facultative Resprouter,
Obligate Seeder A, and Obligate Seeder B, were
maintained with the greatest cover and on the
appropriate landtypes under the Frequent fire
regime.
The maintenance of all functional groups under
the Frequent fire regime is consistent with the
literature that suggests a fairly high natural fire
frequency and diverse community composition for
chaparral. However, previous studies would also
suggest that the fire cycle, in the long term, is
much more variable than the Frequent regime
that was simulated. Further, the results for some
functional groups seem inconsistent with other
modeling and small-scale studies that found midsuccessional species maintained by intermediate
disturbance frequencies (e.g., Denslow, 1980; He
and Mladenoff, 1999b). The literature for southern California shrub communities also predicts
that intermediate fire frequencies will maintain all
of the functional groups used in our simulations,
while very long (\ \ 100 year), or very short
(B B10 year) return intervals would favor resprouters over obligate seeders (Zedler et al.,
1983; Keeley, 1991b).
There are two factors that affect the realism of
our modelling results. First, two of these functional groups, the Obligate Seeders, recruit primarily from fire-cued germination from a seed
bank following stand-replacing fire (when adults
on the site are killed). Because it is not yet possible to simulate this strategy in LANDIS, these
functional groups were dependent upon dispersing
from cells where adults had survived. Therefore,
the performance of these short-lived, poorly dispersing groups under the Moderate and Frequent
regimes was underestimated. Second, the initial
random placement of the extremely shade-tolerant
Obligate Resprouter meant that this group was
always maintained by continuous post-fire resprouting in cells where it was initially placed (no
matter what the disturbance regime) and also by
280
J. Franklin et al. / Ecological Modelling 142 (2001) 261–283
limited dispersal. Therefore the Obligate Resprouter excluded other functional groups from
establishing, to some extent, under all fire
regimes.
Simulations were initiated with a random distribution of functional groups (predominantly single- or few-cell patches) in order to see the effects
of dispersal and establishment. After 50–100 year
larger patches tended to coalesce related to both
fire patterns and the dispersal and establishment
of functional groups onto landtypes for which
they have high establishment probabilities. The
exception was the Obligate Resprouter, owing to
its low probability of establishment on any
landype. The spatial pattern of fires and functional group patches over a model run was
strongly related to the pattern of landtypes and
landscape age (previous fires). The dataset developed for this simulation, in addition to being
simple to interpret, illustrated the degree to which
the spatial patterns predicted by the model are
controlled by patterns of species and landtype.
Initially, with a random distribution of functional
groups and uniform age, fires tended to be circular (this can be seen for some functional groups in
Fig. 4 and Fig. 5). However, once functional
groups dispersed to landtypes for which they had
strong affinities, fire boundaries coincided with
landtype boundaries, as well as old fire scars
(patches of particular ages and fire severity class).
This pattern is characteristic of fuel-driven fire
cycles (Chou et al., 1993) that can also show
spatial patterning (fire shape) related to wind
direction and terrain (Minnich and Chou, 1997).
However, some studies have suggested that the
fire regime is not fuel-driven in this region (Keeley
et al., 1999; Zedler and Seiger, 2000).
LANDIS does not simulate individual fire behavior, and it is not clear to what extent this will
limit its ability to simulate landscape level patterns of fire and succession in southern California
given that fire behavior is strongly tied to topography and weather (Zedler and Seiger, 2000).
However, terrain can be used to define landtypes,
and the model’s fire severity curves can be
modified (flattened) to reflect the ability of even
young shrub stands to burn in California, given
the right conditions. These modifications, as well
as simulating fire-stimulated germination from a
seed bank, will be the subject of future research,
when LANDIS is applied to more realistic landscape patterns in southern California.
Acknowledgements
We are grateful to J. Keeley, R. Minnich, and
P. Zedler for their guidance and comments, and
to C. Coulter, H. Johnson, J. Miller, D. McKinsey and D. Shaari for their assistance. This work
was funded by NSF grant SBR-9818665 to JF.
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