CALIFORNIA STATE UNIVERSITY, NORTHRIDGE
SHIFTS IN CHAPARRAL SPECIES DOMINANCE IN A MEDITERRANEAN
ECOSYSTEM UNDER A CHANGING FIRE REGIME
A thesis submitted in partial fulfillment of the requirements
For the degree of Master of Arts in Geography,
Geographic Information Science
By
Luis Aguilar
December 2015
The thesis of Luis Aguilar is approved:
_______________________________________
__________
Dr. Helen Cox
Date
_______________________________________
__________
Dr. Julie Laity
Date
_______________________________________
__________
Dr. Amalie Orme, Chair
Date
California State University, Northridge
ii
ACKNOWLEDGEMENTS
I would like to thank my thesis committee, Amalie Orme, Helen Cox, and Julie
Laity, for their support. I would like to give a special thanks to Dr. Amalie Orme for
going above and beyond in encouragement, support, and collaboration. Not only was she
a great sounding board, but her ideas and input were very insightful and fruitful.
Thanks to my friends and family who were supportive and understanding of my
absence as I sequestered myself on the last stretches of my thesis work. Their moral
support was much appreciated, and their encouragement did not go unnoticed.
Finally, I would like to extend my gratitude to the various NPS employees at the
Santa Monica Mountains National Recreation Area who made themselves available to
offer guidance and support. Their intimate knowledge of the natural resources of the
Santa Monica Mountains proved to be invaluable. To everyone who had the slightest
involvement in this project, thank you!
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TABLE OF CONTENTS
Signature Page ……………………………………………………………………………ii
Acknowledgments ……………………………………………………………………….iii
List of Tables …………………………………………………………………………….vi
List of Figures ………………………………………………….......................................vii
Abstract ……………………………………………………………………….……….....ix
Section 1: Introduction ……………………………………………………………………1
1.1 Study Site ……………………………………………………………………..2
1.2 Study Species ………………………………………………………………....5
1.3 Changing Fire Regime …………………………………..................................9
1.4 Vegetation Change in a Mediterranean Ecosystem …………………………10
1.5 Response of Mediterranean Species to Fire …………………………………11
Section 2: Data and Methodologies ……………………………………………………..15
2.1 Vegetation Data ………………………………..............................................19
2.2 Fire Data ……………………………………………………………………..23
2.3 Drought Index Data …………………………………….................................27
2.4 Field Data ……………………………………………………………………29
Section 3: Results ………………………………………………………………………..40
3.1 1934 – 2004 Fire Frequency Analysis ……...………………..……………...43
3.2 1934 – 2004 Fire Frequency/RDI Analysis …………...…………………….48
3.3 2004 – 2014 Analysis …………………………………..……………………51
Section 4: Discussion ……………………………………………………………………56
iv
4.1 Conclusions ……………………………………………………….…………58
References ……………………………………………………………………………….60
v
LIST OF TABLES
Table 1: 2004 Vegetation crosswalk …………………………………….………………21
Table 2: General vegetation community change matrix ……...………………...…….…42
Table 3: RDI values per vegetation type following a fire …………….…………………51
Table 4: Dominance cover in 2004 and 2014 …………………………...…….………...52
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LIST OF FIGURES
Figure 1: Study area in the Santa Monica Mountains of southern California …………....3
Figure 2: Study area aerial imagery ……………………………………………................5
Figure 3: Adenostoma fasciculatum ……………………………………………….……...7
Figure 4: Ceanothus megacarpus ……………………………………...………...…….....8
Figure 5: Response to fire ……………………………………………………………….12
Figure 6: Wieslander Vegetation Type Map ………………………………………..…...16
Figure 7: Santa Monica Mountains Vegetation Map ………………………………..…..17
Figure 8: Determining overlapping fire boundaries ……………………………………..23
Figure 9: Calculating fire frequency …….........................................................................26
Figure 10: RDI trends ……………………………………………...................................28
Figure 11: 2014 field revisit draw ………………………………………………...……..30
Figure 12: Grid of 2014 site revisits ………………………………………………….....31
Figure 13: 2014 field revisits grid A …………………………………………………….31
Figure 14: 2014 field revisits grid B …………………………………………………….32
Figure 15: 2014 field revisits grid C ……………………………………….…...……….33
Figure 16: 2014 field revisits grid D …………………………………….….………..….34
Figure 17: 2014 field revisits grid E ……………………………….…………..………..35
Figure 18: 2014 field revisits grid F ……………………………..……………..…….....36
Figure 19: 2014 field revisits grid G …………………………………………………….36
Figure 20: 2014 field sites size distribution ……………………………………………..37
Figure 21: Field polygon delineation …………………………………………………....39
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Figure 22: Urban development from 1934 to 2004 ………………………….………….40
Figure 23: Shift in Chaparral species dominance …………………………….……....…42
Figure 24: Numbers of fires per year …………………………………………..…..……44
Figure 25: Fire frequency (1925-2009) in the Santa Monica Mountains …………..…...45
Figure 26: Fire frequency and vegetation dynamics from 1934 to 2004 ………………..46
Figure 27: Percent of fire frequency area burned by vegetation type ……….…………..48
Figure 28: Subset of single burn areas ………………………………………………..…50
Figure 29: Average RDI per vegetation type …………………………………………....51
Figure 30: Analysis of 2004 A. fasciculatum sites ……………………………….……...54
Figure 31: Analysis of 2004 C. megacarpus sites ………………….…………………...55
viii
ABSTRACT
SHIFTS IN CHAPARRAL SPECIES DOMINANCE IN A MEDITERRANEAN
ECOSYSTEM UNDER A CHANGING FIRE REGIME
By
Luis Aguilar
Master of Arts in Geography,
Geographic Information Science
Over a span of 70 years, portions of the Santa Monica Mountains in southern
California have seen a change from an Adenostoma fasciculatum dominated chaparral
community to a co-dominant A. fasciculatum – Ceanothus megacarpus community. Each
of these species has a different adaptation to fire. A. fasciculatum is a facultative seeder,
while C. megacarpus is an obligate seeder. In conjunction with 70 years of vegetation
modification by humans, environmental conditions also have changed: fire frequency has
increased, and drier conditions are becoming more common.
Recognizing that species compositional change in the Santa Monica Mountains
chaparral community may be somewhat unexpected, this study attempts to identify the
drivers of this apparent transformation. It hypothesizes that the observed trends can be
explained by increased fire frequency and drought. Consequently, it is expected that C.
megacarpus will perform better than A. fasciculatum under increasing fire frequency and
drought. Using GIS, an overlay analysis was conducted to identify the areas where
conversion of dominant species occurred, together with related fire history. Two datasets
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were examined for species composition and fire history. The first dataset referenced
vegetation from1934 to 2004, and was analyzed using a Geographic Information System
(GIS) program while the second consisted of field data acquired in 2004 and in 2014.
In contradiction to the stated hypothesis, the analysis suggested that A.
fasciculatum was better adapted for higher fire frequency than C. megacarpus. This was
observed in the 1934/2004 dataset. The 2004/2014 data set did not yield enough areas of
change to analyze the relationship between vegetation dynamics and fire frequency. The
complexity of this system could not be sufficiently explained by this study, but the
research did suggest that A. fasciculatum is better adapted to increased fire frequency and
drought than C. megacarpus.
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SECTION 1: INTRODUCTION
It has been suggested that Mediterranean ecosystems are experiencing changes in
fire frequency regimes in favor of shorter fire intervals owing to anthropogenic effects
(Vanniere et al. 2008). As urban development encroaches into wildland environments, the
susceptibility to human induced fires increases. Compounded by more variability in
temperature and rainfall, such changes in the fire regime may have adverse effects on
native plant populations. Although species in Mediterranean ecosystems are adapted to
fire, shorter than normal fire return intervals can impact native plant populations. Given a
short enough fire interval, some species may not be able to reach peak reproductive
maturity, and, as a result, some fire-adapted species may be extirpated (Regan et al.
2010). The resultant changes in vegetation communities can have significant effects in an
ecosystem with a possible decrease in species richness through changes in allelopathic
adaptations of the resulting community (McPherson and Muller 1969). It is important for
land managers to understand the potential impacts of changes in vegetation communities
so as to make informed decisions regarding monitoring of these resources, as well as for
understanding implications regarding fire susceptibility, post-fire recovery, adjustments
to slope hydrology, and percent vegetation cover.
The purpose of this study is to determine the primary mechanisms that have
driven an apparent change in chaparral species dominance in a southern California
Mediterranean ecosystem. This study examines the response to fire of two species with
different fire adaptations, Ceanothus megacarpus, an obligate seeder, and Adenostoma
fasciculatum, a facultative seeder. While the physical responses of A. fasciculatum and C.
1
megacarpus post fire are understood, a study that examines competition between these
two species post fire has not been conducted. Obligate seeders are species that generate
completely from seed, while facultative seeders are plants that use both seedling
recruitment and resprouting post-fire. Facultative seeders are generally favored over
obligate seeders under normal post-fire conditions (Guo 2001), but areas where obligate
seeders have been favored in a span of 70 years (1934-2004) have been identified in the
Santa Monica Mountains, southern California (Aguilar and Taylor 2011).
At the landscape scale, discrete areas of the Santa Monica Mountains have been
undergoing a change in the relationship between relative abundance of C. megacarpus
and A. fasciculatum. In an attempt to explain the increase of obligate seeders in the Santa
Monica Mountains, this project examines whether shorter fire return intervals as well as
fire events followed by drought will favor the obligate seeder species C. megacarpus over
the facultative seeder species A. fasciculatum.
1.1 Study Site
Located in a Mediterranean ecosystem, the Santa Monica Mountains of southern
California (Fig. 1) experience dry summers and wet winters. The east-west trending range
extends through parts of Los Angeles and Ventura Counties and is in close proximity to
major metropolitan areas. The Santa Monica Mountains are managed by multiple
agencies including the National Park Service, California State Parks, public and private
conservancy agencies, and local governments.
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Figure 1. Study Area in the Santa Monica Mountains of southern California. Illustrated is the
location of the 1934 Wieslander Vegetation Type Map boundary in red, the 2004 Santa Monica
Mountains National Recreation Area Vegetation Map boundary in blue, and the intersection of
the two boundaries in yellow at a regional scale. The yellow boundary makes up the study area
for this research. The study area lies against the southern coast of California, and is part of a
Mediterranean type ecosystem.
3
The Santa Monica Mountains are continuously undergoing development and
habitat fragmentation (Swenson and Franklin 2000). As the surrounding areas continue to
exhibit growing populations, development in the Santa Monica Mountains is expected to
continue. Models suggest that at varying degrees, encroachment into wildlands is
expected to increase in the next 50 years (Swenson and Franklin 2000, Syphard et al.
2007).
The study area is defined by two vegetation maps, the 1934 Wieslander
Vegetation Type Map (2014) and the 2004 Santa Monica Mountains National Recreation
Area Vegetation Map (Fig. 2). The area analyzed for the 1934-2004 assessment was
defined by the intersection of the two vegetation map datasets. Field surveys were
conducted in 2014 to analyze vegetation change from 2004-2014. The field sites for the
2004-2014 analysis were a randomly selected subset of areas that were surveyed in 2004
to create the Santa Monica Mountains National Recreation Area Vegetation Map.
4
Figure 2. Study area aerial imagery. This figure illustrates the boundaries for the 1934 and 2004
vegetation maps, as well as their intersection (yellow). The area of intersection forms the basis for
this thesis.
1.2 Study Species
The two fire-adapted species examined for this study were Adenostoma
fasciculatum var. fasciculatum (Chamise) in the Rosaceae family, and Ceanothus
megacarpus var. megacarpus (Bigpod Ceanothus) in the Rhamnaceae family. Fire
adaptations vary across a spectrum. On one end, there are obligate seeders like C.
megacarpus (species incapable of resprouting, which die off in a fire and regenerate
completely from seedlings), and on the other end there are resprouters like Malosma
laurina (Laurel Sumac) and Rhus integrifolia (Lemonade Berry), which have very low
seedling recruitment rates and resprout vigorously after a fire. Somewhere in between are
5
the facultative seeders like A. fasciculatum. These are species that have mixed
adaptations. A. fasciculatum is a successful resprouter, but it also reproduces through
seedlings.
Native to California and confined to western North America, A. fasciculatum (Fig.
3) is a multi-stemmed evergreen shrub. It blooms from June to August, and often forms
very dense canopies (Calflora 2014). As a facultative seeder, the primary regeneration
mechanism for A. fasciculatum is resprouting from a lignotuber after fire. Secondary to
resprouting as a method of post-fire recovery is seeding. The species’ longevity is
estimated to be between 100 and 200 years (United States Forest Service 2015).
Currently, A. fasciculatum is the most common chaparral species in the Santa Monica
Mountains with C. megacarpus as a co-dominant in the community. A. fasciculatum has a
larger regional extent than C. megacarpus, occurring as far north as Redding, California,
and extending into the eastern and western foothills of the Sierra Nevada (Calflora 2014).
6
Figure 3. Adenostoma fasciculatum var. fasciculatum in Topanga State Park. (Photograph, T.
Valois, 2004)
C. megacarpus (Fig. 4) is a large evergreen shrub which blooms from February to
April (Calflora.org). Its range extends from Santa Barbara County to San Diego County,
California. Unlike A. fasciculatum, C. megacarpus is not fire resistant, and it does not
resprout after fire. As an obligate seeder, it relies almost completely on fires for
propagation, with its seeds lying dormant but viable for years in the seedbank below
mature stands, requiring heat to scarify. Quick and Quick (1961) found 80% germination
in seedbanks 15 to 20 years old. Because of drought sensitivity and competition and
predation, seedlings experience high mortality during the first year after a fire
(Schlesinger and Gill 1978). The subsequent years present additional challenges for
young C. megacarpus plants. Because it takes several years for individuals to develop
7
extensive root systems, young stands - less than six years of age - are more susceptible to
drought than mature stands (Schlesinger and Gill 1980).
Figure 4. Ceanothus megacarpus var. megacarpus in Topanga State Park. (Photograph, T.
Valois, 2004)
Because C. megacarpus is not fire resistant, stands tend to be comprised of sameage individuals. Stand age is usually determined by the last fire (Schlesinger and Gill
1978). Consequently, it is difficult to find individuals whose ages exceed fire return
intervals. The potential age of C. megacarpus in the absence of fire is unknown, but
Montygried-Loyba and Keeley projected it can live to over 100 years (1987). The largest
mortality event of the C. megacarpus life cycle occurs after a fire, when seedlings are
highly susceptible to predation and competition from other species (Montygried-Loyba
8
and Keeley 1987). Montygried-Loyba and Keeley (1987) found that C. megacarpus
undergoes a second wave of mortality at 10 years of age as the canopy starts to close and
light become scarcer in the understory. Another sharp increase in mortality can be
expected in stands of about 30 to 40 years of age owing to stand senescence.
1.3 Changing Fire Regime
Fire regimes in Mediterranean ecosystems are changing, with many places,
including the Santa Monica Mountains, seeing an increase in fire frequency. Pausas and
Fernandez-Munoz (2012) found that climatic conditions play a crucial role in the fire
regime of the Western Mediterranean Basin with a positive correlation between increased
drought and fire frequency. Rundel and King (2001) found that altered fire regimes are
associated with increased contact between human development and undeveloped
wildlands. Whether related to natural or anthropogenic factors, the altered fire regime is
adding new pressures to native vegetation (Vanniere et al. 2008). Because fire cycles are
crucial to Mediterranean type ecosystem vegetation, it is imperative that species adapt to
these changes if they are to persist. The Santa Monica Mountains have seen a clear
increase in fire frequency (National Park Service, 2015). As people continue to populate
the area, the number of human-induced fires continues to increase. An example is the
2013 Springs Fire, where ignition was caused by an automobile malfunction. This large
fire burned 9582 ha from the California 101 freeway to the Pacific coast on the west end
of the Santa Monica Mountains.
As fire return intervals become shorter (National Park Service, 2015), the plants
that inhabit these burn areas have less time to recover after a fire. This can have
9
deleterious effects on a plant community. Thanos and Daskalakou (2000) found that the
reproductive success of a Mediterranean pine species (Pinus halepensis) decreased as fire
frequency increased, limiting the ability of these plants to reach maturity and produce
seeds.
1.4 Vegetation change in a Mediterranean Ecosystem
Researchers have studied vegetation change at the landscape scale and have found
significant correlations between shifts in plant dominance and land use, fire suppression,
fire frequency, fire size and heat index, soil disturbance, exposure to solar radiation, and
substrate hydrology. Using data from 1974 and 1989, Callaway and Davis (1993)
identified a series of variables that affected rates and trends in vegetation change in
Gaviota State Park, Santa Barbara County, California. They modeled annual rates of
vegetation change from their 1974 and 1989 datasets. Aerial imagery was used to map
general vegetation classes with similar physiognomic and taxonomic characteristics. To
demonstrate that vegetation change is a dynamic part of Southern California’s
Mediterranean ecosystem, they developed numerical models which demonstrated sage
scrub conversion to woodland and chaparral, chaparral conversion to sage scrub and
woodland, and woodland conversion to sage scrub and chaparral. While they determined
that vegetation change occurred in the absence of fire, they found that fire was the most
important driver of change amongst their variables.
In addition to the relationship between fire and vegetation change, an additional
study in Santa Barbara County examined the relationship between vegetation distribution
and ecophysiological processes at the landscape scale. Meentemeyer et. al. (2001) looked
10
at average annual soil moisture, seasonal variability in soil moisture, average annual
photosynthetically active radiation, maximum air temperature, and substrate rockiness in
the Santa Ynez Mountains of Southern California as driving factors in vegetation
distribution. They found that soil moisture played a paramount role, and that A.
fasciculatum exhibited a preference for substrate rockiness over C. megacarpus while C.
megacarpus preferred more xeric environments than A. fasciculatum.
Vegetation change analysis in Mediterranean ecosystems has largely relied on the
use of vegetation maps. Callaway’s and Davis’ (1993) work for example, compared two
temporally different datasets using Geographic Information Systems technologies. They
used spatial datasets from 1947 and from 1989 to interpret and digitize aerial imagery to
determine spatial and temporal change. Others have studied vegetation change by
revisiting sites that have been previously mapped (Forrestel et al. 2011). This project
implemented both the general techniques used by Callaway and Davis (1993) and those
used by Forrestel et al. (2011). Aerial image interpretation has an inherent level of
inaccuracy, but the tradeoff is that it provides a larger spatial coverage than is generally
achieved through field surveys. Field data collection is more accurate, but the associated
labor costs are very high.
1.5 Response of Mediterranean Species to Fire
Knowing a species’ life history and physiology is important for understanding its
possible responses to changing conditions including changes in fire frequency, patterns of
precipitation, and seasonal shifts in microclimate, as these play an important role in plant
adaptation and resilience to stochastic events such as fires (Meentemeyer et al. 2001).
11
Fire adaptations are favorable in fire prone ecosystems (Forrestel et al. 2011), but
adaptations to fire in Mediterranean ecosystems vary between species (Fig. 5); therefore,
the response to a changing fire regime may also vary.
Figure 5. Response to fire. Burn area one year after the 2013 Springs Fire in Point Mugu State
Park. Illustrated is a site of co-occurring A. fasciculatum and C. megacarpus. Some of the
resprouting A. fasciculatum are circled in blue, and the bases of some of the dead non-resprouting
C. megacarpus are circled in green. Basal resprouting is associated with A. fasciculatum, and
while C. megacarpus had no resprouting, C. megacarpus seedlings were present. Because of the
scale, seedlings are not visible in this photograph. (Photograph, L. Aguilar, 2014)
Despite differences in evolutionary trajectories of fire adaptations, species in
Mediterranean ecosystems coexist (Pausas and Keeley 2014). Under current conditions,
each adaptation has its advantages and disadvantages. Energy storage can be very costly
for resprouters, and obligate seeders that rely on recruitment from the seedbank run the
risk of predation from granivores.
Through modeling techniques, Regan et al. (2010) found that Ceanothus greggii
var perplexans, an obligate seeder, has an optimal fire return interval requirement of 3050 years. They modeled fire conditions of 10, 20, 30, and 80 year average fire return
12
intervals. Across all models, they found that C. greggii would become extirpated in a fire
regime defined by 10 year intervals (Regan et al. 2010). Although this species occupies a
higher elevation range than C. megacarpus, it can be seen as a proxy for C. megacarpus,
as it is also an obligate seeder that regenerates completely from seeds post fire.
Monroe and Oechel (1993) tested the effects of fire intensity on A. fasciculatum
under controlled burns. They found that higher fire intensity was followed by greater
mortality rates. They also found a correlation between plant size and mortality. Smaller
plants exhibited higher rates of mortality, while larger plants exhibited lower rates. While
it is uncertain that higher fire frequencies adversely affect A. fasciculatum, it could be
postulated that a high enough fire frequency might result in a population with smaller
sized individuals.
Complicating the above scenarios, when fire events are followed by drought
conditions, the effects can be severe for a plant population. C. megacarpus has been
found to have a higher seedling survival rate than A. fasciculatum when a fire is followed
by drought (Frazer and Davis 1988). While this observed attribute may indicate favorable
conditions for C. megacarpus under changing climatic and fire regimes, the tolerances
and thresholds for obligate seeders and facultative seeders under changing environmental
conditions are unclear. The literature suggests that future compositional makeups may
also be the result of weather conditions in the six years immediately following a fire
(Schlesinger and Gill 1980). Their research found that young C. megacarpus plants are
more susceptible to drought than older plants whereby water potential decreases more
rapidly in younger individuals. A decrease in water potential limits transpiration (the
13
ability of a plant to move water from the root system throughout the body) which inhibits
photosynthesis. This places a plant at a higher risk of mortality.
14
SECTION 2: DATA AND METHODOLOGIES
To determine the changes in distribution of C. megacarpus and A. fasciculatum,
the two dominant chaparral species in the Santa Monica Mountains, two vegetation maps
in a Geographic Information System (GIS) database format (1934 and 2004) and a GIS
fire history map were examined. The latest of the two vegetation maps was used to select
a subset of sites that were visited once between 2002 and 2004 by the Santa Monica
Mountains National Recreation Area to validate the 2004 vegetation map. This subset
selection was revisited and reassessed in 2014 for this study. This allowed for a
comparison between conditions in 2004 and 2014. Additionally, weather data from 1934
to 2004 was examined in order to determine the effects of drought after fire on vegetation
change.
The first of the two vegetation maps was a digitized subset of the Wieslander
Vegetation Type Map (VTM), a natural vegetation survey of California initiated in 1926
by the United States National Forest Service. In an extensive effort that covered over 1/3
of California (vtm.berkeley.edu/#/about), a U. S. Forest Service group lead by Forester A.
E. Wieslander produced a VTM covering the Santa Monica Mountains in 1934. The
original VTM product from the U. S. Forest Service effort was a series of paper maps
that, when combined, created a single vegetation map. From 2002-2003, the Santa
Monica Mountains National Recreation Area contracted Aerial Information Systems
(AIS) to conduct a pilot project that would test the feasibility of georeferencing and
digitizing these paper maps. This work yielded a digital format of a small subset of the
Santa Monica Mountains. In 2008, AIS was contracted to finish digital conversion of the
15
paper maps for the remaining unmapped areas of the mountains. An approach involving
multiple steps conducted by the Santa Monica Mountains National Recreation Area
consisted of georeferencing, aerial image interpretation, and hands-on manipulation to
produce a final vegetation map reflecting the vegetation conditions in 1934 (Fig. 6).
Figure 6. Wieslander Vegetation Type Map. This figure illustrates 1934 vegetation generalized
within the study area. Although the original Wieslander Vegetation Map had more vegetation
classes than the ones indicated in this map, the vegetation classes were aggregated to produce a
generalized set of attributes that could be directly compared to the Santa Monica Mountains
National Recreation Area Vegetation Map of 2004. The vegetation type “CSS” is Coastal Sage
Scrub.
The second vegetation dataset was the Santa Monica Mountains Vegetation Map
(Fig. 7). The map was created by the Santa Monica Mountains National Recreation Area
by using a combination of aerial imagery interpretation and field validation techniques.
Starting in 2001, the National Park Service took on an ambitious project to map
16
vegetation types across the Santa Monica Mountains. Intensive field verification efforts
persisted year-round from 2002 to 2004. Because the last assessments were conducted in
2004, this document is referred to as the 2004 Vegetation Map. The final GIS product
was released by the Santa Monica Mountains National Recreation Area in 2007.
2004 Generalized Vegetation
Figure 7. Santa Monica Mountains Vegetation Map. Vegetation classes were generalized to
allow a direct comparison with the Vegetation Type Map. Although the original vegetation map
had more detailed classes, the classes were aggregated to facilitate the comparison. The
vegetation type “CSS” is Coastal Sage Scrub.
The 2004 vegetation mapping project was undertaken in an effort to produce a
new vegetation classification and map, as well as to provide a useful tool for resource
managers (Keeler-Wolf and Evens 2006). The product was achieved through a joint
effort between the National Park Service (NPS), Environmental Systems Research
Institute, Inc. (ESRI), the California Native Plant Society (CNPS), and Aerial
Information Systems (AIS). The CNPS Rapid Assessment method was used to collect
17
field data (Keeler-Wolf and Evens 2006). A CNPS Rapid Assessment is a
reconnaissance-level method of vegetation sampling. It is a quick way to quantifiably
assess vegetation composition and distribution across a large area. Rapid Assessment
readings include various variables like environmental descriptors (elevation, aspect,
slope, etc.), and vegetation descriptions such as community type, vegetation cover and
species composition. This methodology was chosen because it allowed the National Park
Service to visit the multiple plots in a timely fashion. A Relevé protocol was also
conducted on a subset of the Rapid Assessment sites to assess the accuracy of the Rapid
Assessment protocol (Keeler-Wolf and Evens 2006). Rather than looking at particular
stands of vegetation, the Relevé protocol examines individual 400 m² plots. The 20x20m
plots were assessed for the same variables as the Rapid Assessment protocol. The Relevé
protocol produces more accurate results, but the Rapid Assessment can provide inference
to areas significantly larger than the 400 m² surveyed when using the Relevé protocol.
The Relevé protocol was a very time consuming procedure, but it did yield similar results
to those of the Rapid Assessment protocol, suggesting the results of the larger assessment
were probably accurate.
The GIS fire map was produced by the Santa Monica Mountains National
Recreation Area. The dataset illustrates all known fires since the early 1900s, and it is
continuously updated as more fires occur. Coverage within the study area indicates 431
different fires since records were initiated.
In order to determine the effects of drought after fire on vegetation change,
drought conditions for the period of 1934 to 2004 were examined using the
Reconnaissance Drought Index (RDI). The RDI is an index that illustrates relative
18
drought conditions in a select area for a specific period of time. The index is based on a
relationship between precipitation and evapotranspiration (Tasakiris and Vangelis 2005).
An RDI value of 0 indicates average conditions, negative values indicate dry conditions,
and positive values indicate moist conditions. The farther an RDI value is from the
average (0), the more severe the conditions (drought for the negative values, and wet for
the positive values). RDI values are the ratio of precipitation to evapotranspiration
divided by the average of this ratio minus one (climateanalyzer.org). It is calculated using
the formula ((p/pet)/(avg-p/avg-pet)-1) where p = precipitation, pet = potential
evapotranspiration, avg-p = the average precipitation for the timeframe selected, and avgpet = the average potential evapotranspiration for the timeframe selected to calculate RDI
values. In one study, Pashiardis and Michaelides (2008) found that results for the RDI
were very comparable to the Standardized Precipitation Index (a probability index of
drought that only considers precipitation), but because the RDI accounts for
evapotranspiration, it makes for a more accurate indicator (Tasakiris and Vangelis 2005).
2.1 Vegetation Data
Several factors had to be considered when analyzing the two vegetation datasets.
Given the limited technological resources in 1934, the Wieslander Vegetation Type Map
was coarser than the 2004 Santa Monica Mountains National Recreation Area Vegetation
Map. A total of 25 species were considered in the shrub type community in the Santa
Monica Mountains for the Wieslander Vegetation Type Map, with up to the 6 most
common species noted for each vegetation polygon.
19
A subset of polygons which were dominated by either A. fasciculatum or C.
megacarpus was selected from the 1934 map for analysis. However, A. fasciculatum
polygons with C. megacarpus also listed in the top three dominant species were rejected
to reduce ambiguity and increase accuracy in the analysis. Similarly, C. megacarpus
polygons with A. fasciculatum as one of the top three dominant species were rejected.
The 2004 map is a more data intensive product with a higher spatial resolution
than the 1934 vegetation map. In order to do a one-to-one comparison of the two
vegetation maps, vegetation types had to be generalized. Unlike the 1934 vegetation map,
the 2004 map consisted of multiple alliances, that is, vegetation communities of a
particular makeup determined by the dominant species. Alliance boundaries were
determined by homogeneity within a vegetation stand. The original 2004 vegetation map
consisted of 124 different alliances.
For the purposes of this study, those alliances dominated by C. megacarpus were
grouped into a single C. megacarpus type, and all A. fasciculatum dominated alliances
were grouped into a single A. fasciculatum type. Alliances by name were not enough to
determine which polygons would be included in each vegetation type. Alliances were
crosswalked based on species dominance (Table 1). Detailed categories were assigned a
generalized class (C. megacarpus or A. fasciculatum). This allowed for a direct
comparison between the 1934 and the 2004 vegetation datasets. An overlay analysis was
used to determine the temporal relationship between different variables and vegetation
composition change over time between A. fasciculatum and C. megacarpus. This routine
in the ArcGIS environment produces a layer file that illustrated vegetation dynamics
between 1934 and 2004. The intersection tool (a form of overlay analysis in ArcGIS)
20
computed a geometric intersection between the two vegetation maps. This resulted in a
layer that indicated what the vegetation type was in 1934 and in 2004 for any area within
the study area boundary. From this overlay four target areas were selected for further
analysis. These were areas that illustrated no change in the A. fasciculatum types, types
where C. megacarpus had shifted to A. fasciculatum, types where A. fasciculatum had
shifted to C. megacarpus, and types of unchanged C. megacarpus.
Alliance
Alliance Description
Adenostoma
fasciculatum
Shrubland
Alliance
Characterized by a dominance of Adenostoma
fasciculatum (average of 28.4% cover – low 7.5%
and high 60%). The following two most dominant
species included Salvia mellifera (average 2% cover
– low 0.2% and high 9%), and Malosma laurina
(average 1% cover – low 0.2% and high 6%). 77
sites were assessed to classify this vegetation type
Adenostoma
Characterized by dominance of Adenostoma
fasciculatum- sparsifolium (average 13.6% cover – low 2% and
Adenostoma
high 25%), Ceanothus crassifolius (average 12.8%
sparsifolium
cover – low 4% and high 25%), and Adenostoma
Shrubland
fasciculatum (average 9.8% cover – low 2% and
Alliance
high 24%). 12 sites were assessed to classify this
vegetation type.
Adenostoma
Characterized by dominance of Adenostoma
fasciculatum- fasciculatum (average of 23.8% cover – low 13%
Arctostaphylos and high 40%), Arctostaphylos glandulosa (average
glandulosa
15.2% cover – low 4%, and high 28%), and
Shrubland
Malosma laurina (average 1.3% cover – low 0.2%
Alliance
and high 9%). 25 sites were assessed to classify this
vegetation type.
Adenostoma
Characterized by dominance of Adenostoma
fasciculatum- fasciculatum (average 22.3% cover – low 18% and
Arctostaphylos high 28%), Arctostaphylos glauca (average 17.8%
glauca
cover – low 15% and high 20%), and Ceanothus
Shrubland
megacarpus (average 7.3% cover – low 4% and high
Alliance
15%). 4 sites were assessed to classify this
vegetation type.
Adenostoma
Characterized by dominance of Ceanothus
fasciculatum- crassifolius (average 22.7% cover – low 7% and
Ceanothus
high 45%), Adenostoma fasciculatum (average
21
Generalized
Vegetation
Type
Adenostoma
fasciculatum
Adenostoma
fasciculatum
Adenostoma
fasciculatum
Adenostoma
fasciculatum
Adenostoma
fasciculatum
crassifolius
Shrubland
Alliance
Adenostoma
fasciculatumCeanothus
cuneatus
Shrubland
Alliance
Adenostoma
fasciculatumQuercus
berberidifolia
Shrubland
Alliance
Adenostoma
fasciculatumSalvia
mellifera
Shrubland
Alliance
13.8% cover – low 1% and high 35%), and Salvia
mellifera (average 2.9% cover – low 0.2% and high
15%). 43 sites were assessed to classify this
vegetation type.
Characterized by dominance of Adenostoma
fasciculatum (average 15% cover – low 2.5% and
high 25%), Ceanothus cuneatus (average 11.2%
cover – low 3% and high 18%), and Eriogonum
fasciculatum (average 1.7% cover – low 0.2% and
high 8%). 12 sites were assessed to classify this
vegetation type.
Characterized by dominance of Adenostoma
fasciculatum (average 24% cover – low 6% and high
44%), Quercus berberidifolia (average 7.2% cover –
low 1% and high 12%), and Salvia mellifera
(average 2.5% cover – low 0.2% and high 15%). 20
sites were assessed to classify this vegetation type.
Characterized by dominance of Adenostoma
fasciculatum (average 16.6% cover – low 6% and
high 30%), Salvia mellifera (average 12% cover –
low 5% and high 23%), and Hesperoyucca whipplei
(average 0.7% cover – low 0.2% and high 2.5%). 11
sites were assessed to classify this vegetation type.
Adenostoma
fasciculatum
Adenostoma
fasciculatum
Adenostoma
fasciculatum
Ceanothus
megacarpus
Shrubland
Alliance
Characterized by dominance of Ceanothus
megacarpus (average 44.4% cover – low 12% and
high 75%), Malosma laurina (average 2.3% cover –
low 0.2% and high 9%), and Adenostoma
fasciculatum (average 2.1% cover – low 0.2% and
high 12%). 84 sites were assessed to classify this
vegetation type.
Ceanothus
megacarpus
Ceanothus
spinosus–
Ceanothus
megacarpus
Shrubland
Association
Characterized by dominance of Ceanothus spinosus
(average 22.7% cover – low 5% and high 50%),
Ceanothus megacarpus (average 18.8% cover – low
5% and high 45%), and Malosma laurina (average
3.2% cover – low 0.2% and high 12%). 54 sites were
assessed to classify this vegetation type.
Ceanothus
megacarpus
Table 1. 2004 vegetation crosswalk. Crosswalk of alliances used to generate the generalized A.
fasciculatum and C. megacarpus polygons. Alliances were assigned to either species based on
percent dominance.
22
2.2 Fire Data
To assess the efficacy of increased fire frequency and drought as drivers of
vegetation change, fire frequency values had to be extracted from the Santa Monica
Mountains Fire dataset. This dataset consists primarily of CAL FIRE data. The
assessment of the efficacy of increased fire frequency and drought as drivers of change
was achieved by using a series of geoprocesses in ArcGIS. The first step was to extract
all the fires that occurred between 1934 and 2004. This selection was a two-step process.
First, a query was set to include fires where the year was greater than 1933 and less than
2005. All fires outside this temporal range were excluded. The second step was to run a
selection in ArcGIS for all the fires that intersected the study area boundary. After the
fires of interest were identified, common boundaries shared by multiple fires were
identified using an intersect analysis in the GIS environment (Fig. 8). The result was a
stack of discrete polygons wherever more than one fire occurred.
Figure 8. Determining overlapping fire boundaries. An intersect analysis in ArcGIS determines
overlapping boundaries between different fire events. The resulting stack of polygons with a
shared boundary can then be used to create a fire frequency layer (see figure 9). In this example,
shared boundaries between fire B and fire C will result in a fire frequency of three, and shared
boundaries between fires A, B, and C will result in an area with a fire frequency of three.
23
Multiple overlapping polygons were dissolved into a single polygon with a fire
frequency attribute so as to not inflate results in favor of areas with higher fire frequency.
A dissolve analysis combined all polygons that shared the exact same overlapping
boundaries into a single polygon feature. The fire frequency was assigned depending on
how many polygons were combined. If the polygons were not dissolved, the effects of an
area would be inflated directly proportional to the fire frequency; for example, if a
location had a fire frequency of five and covered an area of 10 ha, the final analysis
should reflect 10 ha, and not 50 ha. If the layer were left undissolved, an area with a fire
frequency of four would be counted four times in the final analysis rather than the actual
single representative time.
Creating a single polygon with an associated fire frequency involved multiple
steps (Fig. 9). After determining the overlapping polygons with a GIS overlay analysis
(Fig. 8), the first step was to convert all fire polygons to points in ArcGIS (Fig. 9) using
the “Feature to Point” tool. Each point represented the geographic center of a fire
polygon. This output resulted in a series of stacked points wherever there were multiple
fires in the original fire history data. The points were then assigned x and y coordinates
using automated calculations in the GIS environment (Fig. 9). Consequently, any set of
overlapping points (points resulting from a series of overlapping polygons) shared a
unique x-y combination. Overlapping points in the point layer could now be combined
into a single point using the dissolved tool in ArcGIS. Points were dissolved by the x-y
coordinate attributes, so all points sharing the same x-y coordinates were combined to
one single point. Each point in this new dissolved layer was assigned a unique identifier
(Fig. 9). This unique identifier would then be used as a unique spatial identifier which
24
was assigned back to the stacked overlapping polygons. The overlapping polygons were
then dissolved by the unique spatial identifier to generate the appropriate boundaries and
the appropriate fire frequency attributes for the fire frequency layer.
25
Figure 9. Calculating fire frequency. A: Convert polygons to points by their centroid. B: Assign
x-y coordinates to point. C: Dissolve points by x-y coordinates. D: Assign unique identifier to
dissolved points. E: Join intersected fire layer and dissolved point layer. F: Dissolve join by
unique identifier and calculate fire frequency.
26
2.3 Drought Index Data
Drought was assessed using the RDI. RDI values were automatically calculated
by climateanalyzer.org using weather data from two National Weather Service
Cooperative Observer Program (COOP) (2014) weather stations (the Santa Monica Pier
station and the University of California, Los Angeles (UCLA) station) and the timeframe
spanning the two vegetation map datasets (1934 and 2004). The values were expressed as
annual averages using the water year (October 1 to September 30). The Santa Monica
Pier station (ID# 47953) is located at decimal degree coordinates (X: 34.008 Y: -118.499)
in the south-eastern end of the study area. The UCLA station (ID# 49152) is located at
coordinates (X: 34.07 Y: -118.443) at the eastern end of the study area, 8 km (5mi),
northeast of the Santa Monica Pier station.
The two weather stations selected for RDI calculations were physically within or
near the study area and were the only stations with continuous data extending back to the
1930s. While detailed records exist for multiple stations in the study area for the last 20
years, data going back to the 1930s was very sparse. Although the UCLA RDI records
were a bit more complete than the Santa Monica Pier records, both stations were
considered in an attempt to reduce the influence of any localized weather anomalies
including the marine influence near the coast and cold air drainage in the interior.
Generally, the two stations exhibited very similar trends (Fig. 10).
27
Figure 10. RDI trends. Distribution of annual RDI values from 1942 to 2009 for the Santa
Monica Pier station (blue) and the UCLA station (red) as calculated by climateanalyzer.gov using
COOP weather stations. The x-axis represents time in rain years, and the y-axis shows RDI
values. Larger RDI values represent wetter than average years, and smaller values represent drier
years. Years where either station was missing values were omitted from this graph. The average
between the two stations is illustrated in green.
Because the records for these two stations were not complete, a regression
analysis was used to estimate missing values. A linear regression was calculated to
facilitate the use of the Santa Monica Pier station as a predictor of UCLA station values.
The correlation coefficient for this relationship produced an r² value of 0.925. Missing
UCLA station values were calculated using the formula y=0.9145x-0.0653, where y is the
modeled UCLA value and x is the known Santa Monica Pier value. A linear regression to
use UCLA station values as predictors of the Santa Monica Pier station resulted in an r²
value of 0.898. Missing Santa Monica Pier station values were calculated using the linear
regression formula y=0.956x+0.0611, where y is the modeled Santa Monica Pier value
and x is the know UCLA value. This method of estimation provided a more complete
dataset. Annual average RDI values of the two stations were then calculated.
28
RDI values were assigned to all areas within the study area that had a fire
frequency value of 1 to assess the effects of drought after fire. Areas with a fire frequency
greater than one would make it difficult to determine the effects of any one drought
event. Because seedling survival is negatively affected by drought during the first five
years post fire (Schlesinger and Gill 1980), drought was examined for the first five
critical years as well as three additional years for comparison. Each fire was assigned the
RDI values for the same year of the fire, one year after the fire and up to seven years after
a fire. Values were assigned depending on the year any given fire occurred. If a fire
occurred in 1990, the RDI value for the year of the fire would be the RDI value for 1990.
That same fire would be assigned the RDI values associated with 1991 to assess
conditions one year after a fire. The same would be applied for all years up to the RDI
value associated with 1997 to assess the effects of RDI conditions seven years after a fire.
2.4 2004 – 2014 Field Data
To obtain the necessary field data, a subset of sites that were visited by the Santa
Monica Mountains National Recreation Area for the 2004 vegetation map were revisited
and assessed using the same Rapid Assessment protocol in 2014. The subset consisted of
sites dominated by A. fasciculatum or C. megacarpus. Sites within private property were
removed from the potential pool of candidates, resulting in a final pool of candidate sites
(Fig. 11) for which two random draws were generated. One draw was generated for all
suitable C. megacarpus dominated plots, and one for all suitable A. fasciculatum
dominated plots. The two random draws were to ensure an equal amount of 2004 A.
fasciculatum and C. megacarpus dominated plots. To randomize each draw, a unique
29
identifier was assigned to each polygon within each of the two groups. Once the groups
had the appropriate unique identifier attribute, a random number generator was used to
determine the field sites that would be included in the study. The first 25 randomly
generated sites in each of the two groups became the field sites for the 2014 reassessment
(Fig 12 - Fig. 19).
Figure 11. 2014 field revisit draw. This figure illustrates all A. fasciculatum and C. megacarpus
plots that were surveyed using the Rapid Assessment protocol for the 2004 Vegetation Map. Also
illustrated is the subset of sites revisited in 2014. A total of 25 A. fasciculatum and a total of 25 C.
megacarpus sites were surveyed in 2014.
30
Figure 12. Grid of 2014 site revisits. This figure illustrates the final draw for the 2014 site
reassessment. A grid (red) was created in order to facilitate the creation of subset maps that were
used to illustrate the field sites visited (Fig. 13 – Fig. 19). The grid allowed for a spatial
contextualization of the various sites.
Figure 13. 2014 field revisits grid A.
31
Figure 14. 2014 field revisits grid B.
32
Figure 15. 2014 field revisits grid C.
33
Figure 16. 2014 field revisits grid D.
34
Figure 17. 2014 field revisits grid E.
35
Figure 18. 2014 field revisits grid F.
Figure 19. 2014 field revisits grid G.
36
Field sites were visited in the order in which their identifier was selected through
random number generation. If any of the first 25 points for either A. fasciculatum or C.
megacarpus were rejected, the next point considered for replacement would be the 26th
randomly generated number; the next, number 27 etc., until the desired sample size of 25
was reached.
The sizes of the polygons for the two vegetation types were very similar (Fig. 20).
The average size for the visited A. fasciculatum polygons was 2.006 hectares, and 1.958
hectares for the C. megacarpus polygons. The minimum size for A. fasciculatum was
0.53 hectares, and 0.34 hectares for C. megacarpus. The maximum polygon size for A.
fasciculatum was 6.17 hectares, and 7.57 hectares for C. megacarpus.
Figure 20. 2014 field sites size distribution. This graph illustrates the size distribution of the
polygons revisited in 2014. Both vegetation types had an average size of 2 hectares.
Once in the field, sites were assessed using the Rapid Assessment protocol. Sites
were assessed for any changes in vegetation boundary delineation, species composition,
37
and percent cover. From a series of vantage points, 2004 polygons were assessed for
change using binoculars. Any changes in boundary were drawn on hardcopy aerial
imagery maps in the field. Pictures were taken to further assist with boundary
delineations.
Generally, boundaries were easy to identify (Fig. 21). In a few cases, the
transitions were much hazier between vegetation types, and more intense field efforts
were needed to decipher the boundary. This included longer field visits as well as
multiple field visits to account for temporary distortions from shadows. Points that fell
within the 2013 Springs Fire boundary were rejected from the analysis. This was because
the time elapsed from the 2013 fire to the 2014 field assessments was not enough to
determine long term compositional change. C. megacarpus would be underrepresented
given the small size of seedlings in comparison to resprouting A. fasciculatum. There was
also no guarantee that the conditions observed one year after the fire would be enough to
determine the eventual dominant composition. Susceptibility to herbivory, drought, and
other variables would make it pointless to guess if a vegetation type would persist in the
future. Like the 1934/2004 datasets, the 2004/2014 field data was analyzed for vegetation
dynamics in conjunction with fire frequency.
38
Figure 21. Field polygon delineation. Franklin Canyon Park, October 2014. Polygon boundaries
identified in the field from a series of vantage points. Dominant species in following polygons 1:
Eriogonum fasciculatum, 2: Ceanothus megacarpus, 3: open grassland, 4: Ceanothus spinosus, 5:
Ceanothus megacarpus (Photograph, L. Aguilar, 2014)
39
SECTION 3: RESULTS
Encroachment on vegetation through urbanization increased in the study area
from 1934 to 2007 (Fig. 22). GIS analysis comparing the two vegetation datasets found
that the general category of “Urban, Agriculture, Disturbed” increased from 14,583 ha to
35,700 ha in a period of 70 years, an increase of total cover from 13% in 1934 to 32% in
2004 within the study area. Development has continued since 2004.
Figure 22. Urban development from 1934 to 2004. Development within and surrounding the
Santa Monica Mountains increased from 14,580 ha in 1934 to 35,492 ha in 2004. 1934 data (top)
from Wieslander Vegetation Type Map and 2004 data (bottom) from the Santa Monica
Mountains National Recreation Area Vegetation Map.
40
The chaparral community accounted for 42% of the study area in 1934 and 31%
in 2004 (Table 2). Parts of the 1934 chaparral underwent type conversion to other
vegetation types including coastal sage, woodland, and grassland, but parts of it were lost
to development. While preliminary research clearly showed that type conversion is a
dynamic process in the Santa Monica Mountains where every general type converted to
every other type (chaparral to grassland and woodland, woodland to grassland and
chaparral, etc.) (Table 2), it did not account for variability of the dominant species within
any given type. The analysis did not examine the relationship between individual species
within the same generalized vegetation type. Missing from Table 1 are the dynamic
interactions within any single community. Of particular interest are the interactions
amongst the dominant species in the chaparral community from 1934 to 2004. There was
a clear change in dominance within the chaparral community. Chaparral converted from
an A. fasciculatum dominated community to an A. fasciculatum - C. megacarpus codominated community (Fig. 23).
41
Table 2. General vegetation community change matrix. The table illustrates change from 1934 to
2004 in hectares within the study area. The first column represents vegetation types in 1934. The
total hectares of any vegetation type in 1934 can be seen in the last column. The total chaparral in
1934 was 46557 hectares. The top row illustrates the vegetation type in 2004. The total hectares
of any given vegetation type can be seen in the bottom row. Chaparral consisted of 34210
hectares in 2004. Any given cell illustrates the relationship between two vegetation types. The
cell in the second column and the fourth row shows that 4536 hectares of coastal sage converted
to chaparral from 1934 to 2004. Values in bold represent areas that did not undergo any change.
(Analysis, Luis Aguilar, 2013)
Figure 23. Shift in Chaparral species dominance. The Santa Monica Mountains have undergone a
significant change in chaparral species dominance in a span of 70 years. While A. fasciculatum
(ADEFAS) (blue) decreased, C. megacarpus (CEAMEG) (green) in the same community has
significantly increased. 1934 values were obtained from Wieslander Vegetation Type Map and
2004 values were obtained from the Santa Monica Mountains National Recreation Area
Vegetation Map.
42
Observations between the 1934/2004 and 2004/2014 analysis produced differing
results. The 2004/2014 analysis posed several restrictions which limited a direct
comparison between the two studies. The areas that underwent fire or experienced type
conversion between 2004 and 2014 were very limited.
3.1 1934 – 2004 Fire Frequency Analysis
Fire frequency increased in the Santa Monica Mountains from 1924 to 2009 (Fig.
24). Some parts of the study area saw up to eleven fires in a span of 89 years (1925 –
2009) (Fig. 25) - an average of one fire every 8 years. All combinations of fire frequency
and vegetation dynamics (areas of change and areas of no change) occurred throughout
the study area (Fig. 26). While changes from C. megacarpus to A. fasciculatum were
observed, the most notable change occurred from A. fasciculatum to C. megacarpus. This
change occurred primarily on the western end of the study area, but it was also observed
throughout most of the southern extent of the study area. Areas of unchanged A.
fasciculatum were concentrated in the middle and northern parts of the study area.
43
y = 0.1212x - 232.27
25
Number of Fires
20
15
10
5
0
1920
1940
1960
1980
2000
2020
Year
Figure 24. Number of fires per year (1925-2009) in the Santa Monica Mountains. Fires in the
Santa Monica Mountains increased in number from 1925 to 2009. The x-axis represents time in
years, and the y-axis represents number of fires. The fire data for this graph was extracted from
the Santa Monica Mountains National Recreation Area GIS dataset.
44
Figure 25. Fire frequency (1925-2009) in the Santa Monica Mountains. Different parts of the
study have burned at different frequencies. As many as 11 fires have burned in some areas from
1925 to 2009. With the exclusion of developed areas, most of the study area has burned at least
once. This map was compiled using the Santa Monica Mountains National Recreation Area fire
dataset.
45
Figure 26. Fire frequency and vegetation dynamics from 1934 to 2004. This figure illustrates the
areas of interest, including areas of change and areas of no change within the study area, as well
as the fire frequency in these areas of interest. Unchanged A. fasciculatum is illustrated in blue, C.
megacarpus to A. fasciculatum in orange, A. fasciculatum to C. megacarpus in red and unchanged
C. megacarpus in green. Lighter shades represent fewer fires, while darker shades represent more
fires. Fire frequency ranges from 0 to 10. For display purposes, the study area was rotated 90º
counterclockwise with east being at the top of the figure.
46
A. fasciculatum showed more persistence following increased fire frequency than
C. megacarpus (Fig. 27). C. megacarpus decreased in percent cover across the study area
as fire frequency increased. There was a decrease in percent cover of A. fasciculatum in
areas with a fire frequency of zero, one, or two, but A. fasciculatum was found to increase
as a proportion of percent cover as fire frequency increased after three fires. Areas where
A. fasciculatum changed to C. megacarpus were found to increase in percent cover as fire
frequency increased in areas with a fire frequency of zero, one, or two fires, but in areas
with a fire frequency greater than two, percent cover decreased as fire frequency
increased. Most of the areas of change from A. fasciculatum to C. megacarpus occurred
in areas with a fire frequency of zero to two fires. Change from A. fasciculatum to C.
megacarpus showed a consistent proportional decrease as fire freqency increased after
the three fire frequency mark. Areas that changed from C. megacarpus to A. fasciculatum
did not show any discernable trends. Fire frequency did not seem to have any particular
effect on areas of change from C. megacarpus to A. fasciculatum as a proportion of cover
throughout the study area. The only observed vegetation dynamic in areas with a fire
frequency from seven to 10 was unchanged A. fasciculatum.
47
Figure 27.Percent of fire frequency area burned by vegetation type. This graph illustrates the
percent makeup by vegetation type in any given fire frequency. Areas with a fire frequency from
seven to 10 were removed from the graph, as the only observed vegetation dynamic in these areas
was unchanged A. fasciculatum. No C. megacarpus was observed in any area that burned more
than six times.
3.2 1934 – 2004 Fire Frequency/RDI Analysis
A subset of areas with a fire frequency of 1 was selected for analysis to discern
the effects of fire and drought on vegetation change (Fig. 28). RDI conditions were
examined for zero to seven years (the time needed for maturation of the species) after a
fire. The exception was one year post fire. The spatially weighted RDI averages for each
of the vegetation dynamics (unchanged A. fasciculatum and C. megacarpus, and areas
where each of these converted to the other) were calculated for every year post fire of
48
interest (Table 3). For most of the analyzed years, climatic conditions did not seem to
influence vegetation dynamics (Fig. 29). With one exception, RDI averages were similar,
within 0.4 of each other. For unchanged A. fasciculatum, RDI values were close to
average for all RDI conditions post fire examined. Areas of unchanged C. megacarpus
also seemed generaly unaffected by RDI conditions. The exception was RDI conditions
one year post fire. The average RDI was -0.6. This was significantly lower than the RDI
values one year post fire for unchanged A. fascicualtum, areas that converted from A.
fasciculatum to C. megacarpus, and areas that converted from C. megacarpus to A.
fasciculatum. Areas where one species changed to the other also seemd to have average
RDI values for the years post fire examined. The exception was one year post fire. Both
of these vegetation dynamics had an average RDI value greater than 1 for one year post
fire. Wet conditions one year after fire corresponded with change from C. meagarpus to
A. fasciculatum, and from A. fasciculatum to C. megacarpus.
49
Figure 28. Subset of single burn areas. This figure illustrates the areas within the study area
boundary that were burned only once between 1934 and 2004. The black represents locations
within the study area that never burned, or burned more than once. Blue represents areas of
unchanged A. fasciculatum (ADEFAS) that burned only once between 1934 and 2004, the green
represents areas of unchanged C. megacarpus (CEAMEG) that burned only once between 1934
and 2004, red represents areas that burned once and changed from ADFA to CEME between
1934 and 2004, yellow represents areas that burned once and converted from CEME to ADFA
between 1934 and 2004, and the gray represents other vegetation types that burned only once
within the study area. No clear patterns were observed, as the different vegetation dynamics were
observed throughout the species’ range in the study area.
50
Figure 29. Average RDI per vegetation type. This graph illustrates the average RDI values per
vegetation type for any given year after a fire (from RDI conditions on the same year of the fire
(YO) to the RDI conditions seven years after a fire (P7)). Analysis was limited to areas with a fire
frequency of 1 between 1934 and 2004.
YO
P1
P2
P3
P4
P5
P6
P7
Unchanged ADFA
0.133
-0.026
-0.118
0.091
0.093
-0.046
-0.028
0.137
0.232
1.157
-0.116
0.069
0.323
-0.012
-0.034
0.278
-0.075
1.051
-0.206
-0.048
0.142
-0.007
0.225
0.203
0.230
-0.608
-0.276
0.069
0.138
0.122
0.000
0.338
CEME to ADFA
ADFA to CEME
Unchanged CEME
Table 3. RDI values per vegetation type following a fire. This table illustrates the average RDI
values associated with each vegetation dynamic for any given time after a fire. YO represents
RDI conditions for the same year of the fire and P# represents the number of years post fire.
3.3 2004-2014 Analysis
The random sample for the 2014 site revisits limited the number of sites that
experienced fires and vegetation change. All sites had a fire frequency of 0 or 1, with
most sites exhibiting the former. Only 12 of the 50 (24%) sites visited in 2014 had
experienced a fire between 2004 and 2014. Further limiting the analysis was the fact that
of the 50 sites, only 2 experienced change (Table 4). Values for 2004 conditions in Table
51
4 were obtained from the Santa Monica Mountains National Recreation Area, and values
for 2014 were those observed during the 2014 reassessment. The observed change
occurred in two plots that were C. megacarpus in 2004. Both sites converted to a Coastal
Sage Scrub community rather than to A. fasciculatum. One site was dominated by
Encelia californica, and the other was dominated by Malosma laurina. These two plots
had a fire frequency of one, and both burned in the 2007 Griffith Park fire.
Assessed
Community
2004
Observed
Change
2014
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
ADEFAS
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
Dom
Sp. %
Cover
2004
55
20
22
4
42
27
23
5
3
25
16
12
30
12
32
12
28
2
7
15
35
20
33
12
58
38
15
33
17
44
30
28
Dom
Sp. %
Cover
2014
50
50
57
40
55
35
62
21
11
70
45
44
52
25
45
24
40
12
22
36
65
60
45
20
45
50
62
65
55
80
33
70
Other
Sp. %
Cover
2004
0
12
29
26
10
22
2
30
32
30
28
8
15
36
20
25
25
19
28
30
13
26
12
18
7
14
30
22
17
16
20
28
52
Other
%
%
If Burned
Sp. %
Open Open
– Year of
Cover
2004 2014
Fire
2014
10
45
40
10
68
40
3
49
45
30
70
30
25
48
20
25
51
40
8
75
30
11
65
68
2005
21
65
68
2005
25
45
5
2005
25
56
30
11
80
45
13
55
35
5
52
70
2006
35
48
20
3
63
73
2005
25
47
35
2005
3
79
85
8
65
70
2005
29
55
35
2005
25
52
10
20
54
20
10
55
45
20
70
60
30
35
25
10
48
40
26
55
11
27
45
8
15
66
30
18
40
2
22
50
45
20
44
10
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
CEAMEG
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
Encelia
californica
Malosma
laurina
40
12
19
22
40
30
18
30
20
23
18
47
10
27
21
23
50
40
9
47
69
70
45
36
15
63
24
35
56
67
58
27
11
33
21
27
15
25
22
18
23
32
8
8
33
24
12
28
20
12
57
38
15
20
40
24
50
34
16
30
4
28
19
36
49
55
60
51
45
45
60
52
57
45
74
45
57
49
67
49
30
48
35
15
16
10
15
40
35
3
60
35
40
5
23
37
8
1
2
40
90
59
2007
15
30
17
15
68
55
2007
2007
2007
Table 4. Dominance cover in 2004 and 2014. This table illustrates the compositional makeup of
any field site in 2004 and 2014. 2004 values were obtained from the Santa Monica Mountains
National Recreation Area, and 2014 values were obtained by the 2014 reassessment. In addition
to the compositional comparison, the table shows any change between 2004 and 2014 in the
“Observed Change 2014” column, as well as the year the area underwent fire.
Percent cover of the dominant species was compared for 2004 A. fasciculatum
dominated plots between 2004 and 2014 (Fig. 30). None of the areas that were A.
fasciculatum in 2004 experienced any change between 2004 and 2014. Eight of the A.
fasciculatum plots burned in either 2005 or 2006. The percent cover of A. fasciculatum
was higher in 2014. Percent of open area decreased in all but two of the A. fasciculatum
plots between 2004 and 2014.
53
Figure 30. Analysis of 2004 A. fasciculatum sites. This graph illustrates the distribution of
dominance percent cover per site in 2004 and 2014. The x-axis represents the 2004 A.
fasciculatum (ADEFAS) dominated plots (dark blue) that were reassessed in 2014 (light blue).
Each pair of bars represents the percent cover of the dominant species in the same area as it was
in 2004 and 2014. The y-axis represents the percent cover value of the dominant species in each
plot. All sites were dominated by A. fasciculatum in 2004 and 2014. A red asterisks above a pair
of bars indicates plots that burned between 2004 and 2014. Percent cover of A. fasciculatum was
higher in 2014 in 23 of the 25 surveyed plots.
Percent cover of the dominant species was compared for 2004 C. megacarpus
dominated plots between 2004 and 2014 (Fig. 31). Four of the C. megacarpus plots
burned in 2007, and two of these plots underwent type conversion.Although the general
trend was that percent cover was higher in 2014 than 2004, that was not the case for all of
the plots. Three of the 23 unchanged plots had higher percent cover in 2004 than 2014.
54
Figure 31. Analysis of 2004 C. megacarpus sites. This graph illustrates the distribution of
dominance percent cover per site in 2004 and 2014. The x-axis represents the 2004 C.
megacarpus (CEAMEG) dominated plots (dark green) that were reassessed in 2014 (light green).
Each pair of bars represents the percent cover of the dominant species in the same area as it was
in 2004 and 2014. The y-axis represents the percent cover value of the dominant species in each
plot. A single red asterisk above a pair of bars indicates plots that burned between 2004 and 2014.
Double asterisks represent areas that both burned between 2004 and 2014, and underwent change
from the dominant C. megacarpus species in 2004 to dominance by another species in 2014. Two
of the 25 plots experienced a change in dominance. One plot converted from C. megacarpus to
Encelia californica, and one converted to Malosma laurina. As a percent cover, C. megacarpus
was more abundant in 2014 that 2004.
55
SECTION 4: DISCUSSION
The original hypothesis that C. megacarpus would perform better than A.
fasciculatum in a higher fire frequency and drier environment was not supported by this
research. Generally, the relationship between increased fire frequency and drought and
vegetation dynamics was weak. While C. megacarpus was expected to be favored in an
environment of increased fire frequency, it seems that A. fasciculatum is more capable of
dealing with this type of environmental change. Drought did not seem to be a driver of
change favoring one species over another. This research shows that RDI conditions one
year after a fire may be significant, in that wet conditions stimulate change both from A.
fasciculatum to C. megacarpus, and from C. megacarpus to A. fasciculatum. These
results were unexpected in that under the same environmental conditions, change would
be expected from one species to another, but not from both A. fasciculatum to C.
megacarpus, and from C. megacarpus to A. fasciculatum. It is possible this may represent
a dynamic which could have been present during the 70 year period and remained
undocumented. This research opens a new gateway for further studies at a level which
heretofore has been unexplored.
The 2004/2014 analysis suggests that time intervals longer than 10 years are
necessary when studying vegetation dynamics in the absence of fire at the landscape
scale. While the observations of increased percent cover of the dominant species may be
the result of maturing individuals, the observed increase in percent cover could also be
due to observer error and/or the lack of calibration between different observers. This
possibility of observer error by lack of calibration as the explanation for increasing
percent cover was suggested by the consistent difference between the 2004 and 2014 A.
56
fasciculatum sites (Fig. 29), although the same consistency in difference of percent cover
between 2004 and 2014 was not observed in the C. megacarpus sites. If not due to
observer error, these observations can be profound in illustrating the different
morphological adaptations of these two species. The vigor in recovery and establishment
of A. fasciculatum may exceed that of C. megacarpus. Age class data is also crucial for
assessing potential differences in recovery response post fire between these two species.
More research is necessary to understand the relationship between these
vegetation dynamics and environmental factors. While this research does suggest that A.
fasciculatum is more resilient to increased fire frequency than C. megacarpus, as well as
favorability for vegetation change between these two species under wet conditions one
year after a fire, the results should be weighed against several considerations. Particularly
with older datasets, a level of uncertainty is always inherent. There are always challenges
associated with vegetation mapping. C. megacarpus can be underrepresented in
assessment after a fire, as seedlings respond more slowly than resprouters. Areas
undergoing change can be missed if the timing favors one species over another.
Working with fire frequency introduces difficulties in data interpretation, as it
excluded any significant events that occurred between fires. Fire frequency is an
important variable to consider when investigating vegetation change in a Mediterranean
ecosystem, but the limitations posed when trying to analyze the effects of fire frequency
should consider other factors such as fire intensity, soil and substrate response to fire, and
the timing and amount of precipitation. Because of the timeframe between datasets, fire
intensity was not considered in this analysis, although it may play a significant role in the
lifecycle of Mediterranean vegetation species. Fire intensity data are becoming more
57
common, as are the tools to conduct fire intensity analysis. Finally, when conducting the
RDI analysis, it was critical to limit the analysis to areas with a fire frequency of 1. All
other areas had to be excluded because averaging values of multiple fire years would
diminish the impacts of any one significant event. As vegetation distribution datasets
continue to develop higher resolution and more continuity, the efforts to understand these
complicated systems should be resolved to a certain degree.
4.1 Conclusions
A. fasciculatum is better suited to increased fire frequency than C. megacarpus.
As fire return intervals are shorter than 23 years (the equivalent of a fire frequency of 3 in
a 70 year span), A. fasciculatum increases as a percent of the total A. fasciculatum – C.
megacarpus dominated chaparral community. C. megacarpus decreased as fire frequency
increased, and no C. megacarpus occurred in areas with a fire frequency greater than 6 (a
fire return interval of 11.6 years). A. fasciculatum was found in areas with the shortest
fire return interval of 7 years (a fire frequency of 10 for the 70 year time span from 1934
to 2004).
With the exception of one year post fire, there were no clear relationships between
drought conditions and time after a fire and vegetation dynamics. On average, above
average precipitation was recorded one year post fire for areas that underwent change
from C. megacarpus to A. fasciculatum, and from A. fasciculatum to C. megacarpus
(38% of the areas that only burned once from 1934 to 2004).
Ten years in the absence of fire is not enough to note significant shifts in
composition between C. megacarpus and A. fasciculatum dominated chaparral
58
communities. No plots underwent conversion between A. fasciculatum dominated
chaparral and C. megacarpus dominated chaparral, but two of the burned C. megacarpus
dominated plots underwent type conversion to other vegetation types. This suggests fire
events may not have much of an impact on change in dominance between A. fasciculatum
and C. megacarpus. The same cannot be said for the effects of fire and change from
chaparral to other general vegetation types such as coastal sage scrub. More research is
needed to determine whether the adaptive mechanisms that induce change within one
generalized vegetation type are the same mechanisms that allow change between general
types.
59
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